>>And welcome to this special cube conversation. I'm John for a, your host of the cube. We're here in Palo Alto, California, and I'm here with a very special guest coming down from Seattle remotely into the cube studios is the leader at AWS Amazon web services, the vice president of database analytics and machine learning Swami. Great to see you cube alumni recently taking over the database business at AWS as a leader. Congratulations. And thanks for coming on the cube. >>Hey, my pleasure to be here, John, very excited to talk to you. >>Yeah. We've had many conversations on the cube and also in person and also online around all the major mega trends. You've had your hand in all the action, going back to your days when you were in school learning and, and writing papers. And 10 years ago, Amazon web services launched AWS dynamo, DB, fast, flexible, no SQL database that everyone loves today, which has inspired a generation of what I would call database distributing cloud scale, single digit millisecond performance at scale. And again, the key scale. And again, this is 10 years ago, so it seems like yesterday, but you guys are celebrating and your name was on the original paper with CTO Verner. Vogel's your celebrity. Congratulations. >>Thank you. Not sure about the celebrating part, but I'm very excited. At least I played a hand in building such an amazing technology that has enabled so many amazing customers along the way as well. So >>Trivia on the, on the paper as you were an intern at AWS, so you're getting your PhD. And then since, since rising through the ranks and involved in a lot of products over the years, and then leading the machine learning and AI, which is now changing the game at the industry level, but I got to ask you getting back to the story here. A lot of customers have built amazing things on top of dynamo DB, not to mention lots of other AWS and Amazon tech riding on it. Can you share some of the highlights that came out of the original paper? And so with some examples, because I think this is a point in time, 10 years ago, where you start to, so the KickUp of cloud scale, not just, just for developers and building startups, you're really starting to see the scale rise. >>Yeah, I actually, I mean, as you probably know, based on what he read to explain the Genesis of dynamo DB itself had to explain the Genesis of how Amazon got into building the original dynamo, right? And this was during the time when miner, I joined Ron esteem as an intern and, and Amazon was one of the pioneers in pushing the boundary of scale. And a year over year, our Q4 holiday season tends to be really, really bad for all the right reasons. We all want our holiday shopping done during that time. And you want to be able to scale your website, arters fulfillment centers, all of them at that time. And those are the times around 2005. And the answer is when people think our database, they think of a single database server that actually runs on a box and has a certain characteristics and does a scale and availability and whatnot. >>And it's usually relational. And then when we had a major disruption during Q4 that's when yeah, ask ourselves the question, why are we actually using a relational database for some of these things when they really didn't need the data model complexity of relational database. And normally I would say most companies where to actually ask an intern or a few engineers who are early in the career saying like, what the hell are you suggesting? Just go away. But Amazon being enabling Buddhists to build what they want. And they actually let us start reimagining what a database or our scale could look like. And that led to dynamo. And since she unstained mine, then we migrated from an traditional relational database stair this one for some of the amazon.com services. And then I moved on to actually start building some butts off our storage service and then our managed relational database service, I explicitly remember. >>And one of our customer advisory board, we're just the set off some of our leading customers who actually give us feedback on roadmap. Another son, Don, who's the CEO and chief geek of spunk bargain faker. And him actually looking at the Trinity me, I was starting in the corner and saying like you all, both tomorrow and why do I need to keep shotting my, my sequel database and reshooting assigned scaling. And this is the time when the state of the art in most databases were around. Like, you start sharding your relational database and constantly reshaping. And this is when most websites are starting to experience the kind of scale which we consider a normal month. During those times it was mostly, most companies used to have a single relational database backend and start scaling that way. And that conversation led entirely under duress, unaided read, lot of AWS leaders and myself saying like, Hey, what is a cloud database reimagined without the hampering SQL look like? And that led us to start building dynamo DB, but just a key value database at that time. Now we support document might've too, but that single digit millisecond latency at any scale imagine. So >>I think about that time at that time, 10 years ago, when you were having this conversation and I know the smug mug and I, he said, he's in totally geek and he's, he's good to point that out. You also have Netflix as customers too. I'd like to hear how that's evolved, but, but I think back at the time, if you look back then I got to ask you most people we've talked about this before. No one database rules, a world that's now standard people now don't see one database back then it was a one database kind of mindset back then. Yeah. And then you had that big data movement happening with Hadoop. You had the object store developing. So you're in you're you're circling around that area. What was it like then? I mean, take, take us through that because there was obvious visibility that, Hey, let's just store this. Now you see data lakes and that's all happening. But back then object store was kind of new. Yeah. >>Ah, it's a great question. Now, one of the things I realized early on, especially when I was working with binary, when you're saying amazon.com itself as an example, that the access patterns for various applications and Amazon, but let alone AWS customers tend to be very, very, very, some of them really just needed an object store. Some of them needed a relational database. Some of them really wanted a key value store within a fast latency. Some of them really needed a durable cash. And, but it so happens when you have a giant hammer. You use that for everything looks like a map, which is essentially the story at that time. And so everyone kept using the same database, irrespective of what the problem was because nobody else, I mean, thought about like, what else can we build that is better? So this let us do, literally I remember writing a paper with Bernard internally that is widely used in Amazon explaining what are all the menu of booklets that access. >>And then how do we go about actually solving for each of these things so that they can actually grow and innovate faster. And, and this was led to actually the Genesis of not only building IDs and so forth, but also dynamo and various other non-relational data. There's a still let alone not so storage access patterns and what not. So, and this was one of the big revelations he had just that there is not a single database that is going to meet the customer, needs us. The diversity of workloads in the internet is growing. And this was a key pivotal moment because with cloud now applications can scale very more instantly than before now. Building an application for Superbowl is very easier than before. That means that on, I mean, everybody is pushing the boundaries of what scale means, and they are expecting more from their obligations. That's when you need technologies like dynamo, DB, and that's exactly what dynamo already be set out to do. And since then, we are continuing to innovate on behalf of our customers and the purpose of the database story as well. And this concept has resonated well across the board. If you see that the database industry has also embraced this method, >>It's natural that you obviously evolved into the machine learning side of it because that's data is big part of that. And you see back then you, you bringing up kind of like flashes for me where it's like those, the data conversations back then and the data movement was just beginning. So the idea that you can have diversity in access methods of the kind of databases was a use case driven by the application, not so much database saying, this is how you have to work, that the script was flipped. It it's changed from infrastructure dictating to the applications, what to do. Now, the applications are going to the infrastructure and saying, give me what I want. I want to access something here in an office store, something here in no SQL that became the Genesis of infrastructure as code at a, at a global level. And so your paper kind of set the, the, the wave, the influence for this, no SQL did big data movement. It's created tons of value, maybe a third Mongo might've been influenced by this other people have been influenced. Can you share some stories of how people adopted the concept of dynamo DB and how that's changed in the industry and how has that helped the industry evolve? >>I mean, plus file data. Most share our experience of building and dynamo style data store. Very, it is a non-relational API and showing what are some of the experiences that the Venter in building such an paper and these set out early on itself, that it is should not be just a design paper, but it should be something that we shared our experiences. So even now, when I talked to my friends and colleagues and various other companies, one thing they always tell me is they appreciated the openness with which we were sharing. Some of the examples and learnings that we learned to not optimizing for percentile latencies, and what are some of the scalability challenges, how we solved and some of the techniques around things like sloppy Cora or various other stuff. We invented a lot of towns along the way too, but people really appreciated several of some of our findings and as talking about it. >>And since then I met so many other innovations are happening in the industry and the AWS, but also across the entire academia and industry in this space, the databases I've been going through what I call as a period of Renaissance, where one of the things, if you see our own arc, when Roger and I started on the database, front Disney started over the promo saying like, if you were to build a database where cloud is the new normal, this is again in 2008, we asked ourselves that question and what the belt that led us to start building things like dynamo, DB, RDS star. I know that alone, we reimagined data viruses with Redshift and several, and then several other databases like time stream for time series workloads started running Neptune for graph and whatnot. But at the moment we started actually asking that question and working backwards from customers. Then you will start being able to innovate accordingly. And this has worked really well. Then more than a hundred thousand AWS customers have chosen dynamo DB for mobile gaming tech IOT. Many of these are fast growing businesses, such as ledge, Darryl BNB, red fan, as soon as enterprises like Samsung Toyota, capital one and so far. So these are like really some meaningful clouds, let alone amazon.com. I run this. >>We have an internal customer is always good to have that entire inside customer. You know, I really find this a really profound use case because you're just talking, you know, in Amazonian terms, I'll just translate for the audience working backwards from the customer, which is the customer obsession you guys have. So here's, what's going on off the way I see it. You got dynamo, DB, paper, you and Verner, and the team Paul was a great as a great video on your blog posts that goes into the, to the talk he gave at around that time, which is fun to watch if you look back, but you have a radical enabler here, that's disrupting and changing S3 RDS, Aurora. These are game-changing concepts inside the, the landscape of AWS at the same time, you're working backwards from the customer. So the question I have for you as a leader and as a builder, how did you balance the working backwards from the customer while bringing something brand new and radical at that time to the market? >>Yeah, this is one of the S I mean hardest things to be, as leaders need to balance on. If you see many times, then we actually worked backwards from customers. The literal later translated this, literally do what customers are asking for, which is true nine out of 10 times, but there is one or a 10 times, you got to read between the lines on what they are asking. Because many times customers when are articulate that they need to go fast. If in the right way, they might say, Hey, I wish my heart storage goes faster, but they're not going to tell you they need a car, but you need to know and be able to translate and read between the lines we call it under the bucket of innovate on behalf of customers. And that is exactly the kind of a mantra we had when we were thinking about concepts like dynamo DB, because essentially at that time, almost everybody would, if I asked, they would just say, I wish a relational database could actually be able to scale from not just like a hundred gigabyte to one terabyte are, it can take up to like 2 million transactions, a second and so forth and still be cheap and made in reality as relational databases, the way they were engineered at that time, those are not going to meet the scale needs. >>So this is fair. We hunted read between the lines on what are some of the key Mustang needs from customers and then work backwards and then innovate on behalf of these workloads, be enabled by the sun oh four, which are some of the reasons that led to us launching some of the initial sets on dynamo on a single digit millisecond latency and seamless scale. At that time, databases didn't have the elasticity to go from like 10 requests, a second to like a hundred thousand or 1 million requests a second, and then scaled right back in an hour. So that was not possible. And we kind of enabled that. And that was an, a pretty big game changer that showed the elasticity of the cloud to a database. Well, >>Yeah, I think also just to, not to nerd out on this, but it enables a lot of other kind of cool scaled concepts, like queuing storage. It's all kind of together. This database piece of that you guys are solving. And again, props to you guys on the team. Congratulations. I have to ask, you know, more generally, how has your thinking changed since the paper? I'll see, you've got more experience under your belt. You don't yet have the gray hairs yet, but we'll see those soon come in, but you know, you're, you got a lot more experience. You're running teams, you're launching a lot of products. How has your thinking changed in the industry since the paper what's happening now? What's the big evolution. What are those new things now that are in the innovate on behalf of the customer? What's between the lines now, how do you see this happening? >>I mean, now since wanting dynamo via a victim, I had the opportunity to work on various problems in the big data space. There we've worked on some are fire things that you might be aware of in the analytics all the way from Redshift to quick side, too. Then I moved on to start some of our efforts, having built systems that enabled customer to store process and credit, and then analyze them. One of the realizations, I had this, the in around 2015 or 2016, I kinda had that machine learning was hitting a critical point where now it is ready for being scaled at option. Their cloud has basically enabled limitless compute and limitless storage, which are the factors that are holding back machine learning technology. Then I realized that now we have a unique opportunity to bring machine learning BI to everybody, not just folks with PhD in machine learning. >>And that's when I moved on from database and analytics areas, they started machine learning. We're just a descent area because machine learning is powered by data and then started building capabilities like SageMaker, which is our end to end ML platform to build, train and deploy them on models. And this, what does the leading enterprise platform by several gaggled users and then also a bunch of our AI services since then, I view the reason I'm giving all this historical context is one of the biggest realization I had early on itself. And 2016 as first machine learning is one of the most disruptive technologies. She will then country in our generation. This is right after cloud. I think these still are the most amazing combination that is going to revolutionize how we build applications and how we actually reason about that. Now, the second thing is that at the end of the day, when you look at the ANC and journey, it is not just about one database or one data Varroa. >>So one data lake product, or even 1:00 AM out platform. It is about the end to end journey where a customer is storing their order database. And then they are actually building a data lake that test customer history and order history. And they want to be able to personalize. And for their viewer experience are actually forecast what products to staff in their fulfillment center, but then all these things need to work and to handle. And that view is one of the big things that struck me for the past five years. And I've been on this journey in addition to building this Emma building blocks to connect the dots so that customers can go on this modern end to end data strategy as I call it, right. It goes beyond a single database technology or data technology, but putting now all of these end to end together so that customers don't end up spending six months connecting the dots, which has been the state of the down for the last couple of years. And we are bringing it down to matter of the Sundays. Now >>He's incredible Swami. Thank you so much for spending the time with us here in the, >>Yeah, my pleasure. Thanks again, Sean. Thanks for having me.

>>Welcome to the cubes coverage of VMworld 2021. I'm Lisa Martin VJ ramen. Shannon joins me next VP of product management at VMware VJ. Welcome back to the program. >>Thank you. So >>We're going to be talking about disaster recovery, VMware cloud. Dr. We've had a lot of challenges with respect to cybersecurity, but the world has in the last 18 months, I'd like to get your, your thoughts on the disaster recovery as a service, the dearest market. What are some of the key trends? Anything that you've noticed have particular interest in the last year and a half? >>Yeah, actually you're right. I mean that the last one year, since the pandemic, you know, the whole, um, lot of industries want to, uh, deploy DLR systems and want to protect themselves in France, somewhere and other, uh, other areas of the Amazon predicting that the disaster service market is going to reach about $10 billion by 2025. And so we, uh, we introduced bandwidth disaster recovery, you know, the last beam work with an acquisition of a company called atrium. And since then we've had tremendous success and it was really largely driven by two key trends that we seen in the market. One is that a lot of our customers have regulatory and mandates to do have a PR plan in place. And second is ransomware and ransomware a lot more in this interview, but ransomware is top of mind for a lot of customers. So those, these two combined together is really making a huge push to, uh, to protect all the data against, uh, disasters. >>What type of customers and any particular industries that you see that are really keenly adopting VMware cloud and D anything that you think is interesting. >>Yeah, it's actually interesting that you say it's actually not a single vertical or a size of the customer. What we have again, what we're finding is that a lot of the regulated industries, I, you know, having 92 to do the art, but the existing VR and data production systems are extremely complex and not cost effective. So, you know, customers are asked to do more with less. And so a lot of our customers, a lot of those customers are asking for, uh, looking for a cost-effective way to protect all the data. And, you know, and ransomware is not something that, that impacts, you know, any single vertical or, or any single size of customer. It impacts everyone. So we're seeing interest from all different verticals, different sizes of customers, uh, across, uh, the, you know, the B cell this, >>Yeah, you're right. The ransomware is a universal problem. And as we saw in the last few months, a problem that is really one of national public health and safety and security concerns. So you mentioned that customers from a regulatory perspective, those that need to implement Dr. Ransomware, as we talked about, are there, and then you also mentioned legacy solutions are kind of costly complex. Talk to me about some of the challenges with respect to those legacy solutions that you're helping customers to address with VMware cloud disaster recovery. >>Yeah. There are a few traits of chains that are, uh, that are emerging and then the whole data production space. One is, uh, customers want to do more with the data. And so with legacy systems, what they're finding is that customers are, you know, are able to replicate the data, but the data is sitting idle and not being used. And so, um, you know, and that's extremely, very expensive for our customers on the line. And secondly, from an outpatient standpoint, backup and Dr, as kind of merging into a single single solution and ransomware protection is becoming a critical use case as we spoke about at the talk about for that. So, uh, customers are not looking to deploy different systems for different types of production. They're looking for a similar solution that, that the lowest cost and gives them enough production across all these different use cases. >>And so where the NFL disaster recovery comes into play is that, is that we are able to use the data that we protect for other uses such as, uh, such as ransomware recovery, such as data protection, such as disaster recovery. So single copy of data that's being could be used in multiple use cases. Number one. And secondly, uh, it's a very expensive, uh, proposition to have, um, you know, on-prem to on-prem, you know, having to, you know, people who shouldn't capacity just sitting idle. And so where Vizio comes into play is that they're able to use, uh, protect the data into cloud, store it in a cost effective manner, and then just use the data when it's acquired either fatal or during disasters in ransomware. And that's where you're able to in, in, in, in the market today, >>Dig through some of those differentiators, if you will, one by one, because there's so much choice out there, there's a lot of backup solutions. Some that are providing backup only some that are doing also Dr. Depending on how customers have deployed and how they're using the technology. But when you're in customer conversations, what are the three things that you articulate about VMware cloud DVR that really help it stand out above the pack? >>Yeah, number one is the cost, right? Um, we, you know, we're able to bring down the cost of, uh, of a disaster protection, uh, by 65, by 65%. And, uh, and, you know, um, that's one big value proposition that we, uh, that we know highlight in our solution. Number two, a lot of our customers also becoming environmentally friendly and, you know, and I'm in a conscious, I should say. And so, because we're able to store the data in a more cost-effective manner, in a more efficient manner in the cloud, they're able to bring down the carbon footprint by 80% compared to regular, you know, your legacy, uh, disaster recovery and data protection solution. And the third, you know, sort of major value proposition from, from, uh, from the BMS is that, you know, we're able to integrate the, uh, uh, BCDR solution, the disaster coriander data protection solution. So well into our, um, you know, into, into the ecosystem, uh, can easily operationally easily recover data into a BM ware cloud. And so for, for the BMA ecosystem, it just becomes a natural logical extension of their, uh, their, uh, toolset. >>That's huge having a console that you're familiar with, you know, the whole point of, of backing up data and the need to recover from a disaster is to be able to restore the data in a timely fashion. I talked with a lot of customers who were using legacy technologies, and that was one of the biggest challenges backup windows weren't completing, or they simply couldn't recover data that was either, um, lost in an, in a ransomware attack or accidentally lost that recovery is what it's all about. Right. >>That's it, that's exactly right. And so at this rainbow ledger using a key enhancements and features that specifically speak to that, uh, you know, to that pain point that you just mentioned, you know, uh, we are bringing down, uh, the, uh, you know, the replication time, uh, to 30 to 30 minutes. So in other words, your Delta is, is, is, uh, is at a 300 interval now compared to all us in a traditional backup system. And number two, um, we are extending, uh, you know, be in love with a copy of it regardless it's always had with single file recovery. And so, especially for the, for the ransomware, uh, use case customers are quickly able to figure out which file leads to the restore, and they're able to restore those files individually rather than restoring their entire VM for the entire data center. And so it becomes a critical, uh, use case for, uh, critical functionality, I should say, for a ransomware recovery. And the other huge announcement of a major announcement media announcement had been made, uh, uh, others be involved is the integration into the VMware cloud in such a way that customers who move are migrating data into the BMR, the cloud on AWS can, uh, have the opportunity to, um, uh, protect the data, um, you know, uh, you know, easily BCDR and >>Got it. I'd love to get an example of a customer that you helped to recover from ransomware. As we mentioned, it's on the rise. In fact, I was looking at some cybersecurity data in the last few weeks, and it's the first half of 2021 calendar. It was up nearly 11 ax. And obviously the, the, the hockey stick lists looking like it's going to continue to go up into the right. So give me an example of a customer that you helped recover after they were hit with ransomware. >>Yeah. Yeah, I lose. And in fact, before I give you one set, one statistic that I just saw recently, um, it is, um, every Lennon are going to be across the board. There's some ransomware attack and in the world. And so, uh, you know, it is a big, you know, it is a huge, huge top of mind for a lot of, uh, the CEO's across and I, you know, across the globe now, uh, we, I just give you an example of one customer that we helped, um, you know, protect the data against ransomware. Merrick is the customer name, uh, it's a public reference. It can, um, you know, it's, it's in the BMI website and they had legacy systems, just like we talked about before they had legacy systems for protecting the data and they had, you know, backup systems and they had disaster recovery systems. >>And the big pain point was that, you know, they knew that they are, you know, they needed to protect against ransomware and, but they had two different systems backup and disaster recovery, and their cost was high because they were replicating the light data or production data, uh, you know, across different sites. And so they were looking for a, uh, to lower the cost of disaster recovery, but more importantly, they're looking to, uh, to protect themselves against potential ransomware threats and, um, and they were able to deploy VCR. And how does multiple points in time? Um, you know, I, in, in, um, in the, in the cloud that are, that allows them to go to any point, uh, you know, uh, after a ransomware attack and record from it. And as I said, the single file recovery was a huge benefit for them because they can then figure out exactly which, you know, which of those files, uh, you know, required, um, recovery. And so, um, they're able to lower the cost and protect, uh, and at the same time, uh, you know, meet the regulatory requirements and mandates to have a production in place so that the women all up there in all over the place, >>As you said, there, the data show one ransomware attack occurs every 11 seconds. And of course we only hear about the ones that make the news, right, for the most part, our customers talk about, Hey, we've had this problem. So it is no longer a, if we get hit with ransomware for every industry, like you were saying before, no industry is blind to this. It's when we get hit, we've gotta be able to recover the data. It sounds like what you're talking about from a recovery perspective is it's, it's very granular. So folks can go in and find exactly what they're looking for. Like, they don't have to restore entire VM. They can go down to the file level. >>That's exactly right. And, and you need the grant of the recovery because you want to be able to quickly restore, you know, your data, uh, and get back on, uh, you know, get back in the business. And so, uh, we provide that granular, granular recovery at the file level so that you can quickly scan your data, figure out which file needs to be at least a bit of cover and recollect just those files. Of course, you can also the color. We also provide authorization for the whole data center for the whole, uh, you know, BM and all the beings in the data center, but customers when they hit the trends and where they want to be able to quickly get back, get back into production, to those flights that, you know, that they critically need. And so that's, um, yeah, that's, it's a critical functionality. >>So is this whole entire solution in the cloud, or is there anything that the customer needs to have on premise? >>So this is, uh, all the data is go to the cloud in an efficient day, in an efficient way. Again, uh, you know, this is another sort of, um, like be that behalf, which is it's easy to just store data in the cloud in a debate, but what we do is be efficiently store the data so that, you know, you, uh, you know, you can know what the cost of your storage and, uh, uh, in the cloud. And so, you know, we used to be at BCDR, we'll be in the cloud disaster recovery. Those data in the cloud is, uh, and, and, and the data repository is in the cloud. And, uh, you can either recover data back to where you need to recover, or we allow filo or orchestrate automatically feel or of, uh, workloads into VMware on AWS, again, operational consistent, because it's a BMI software that's running on ground BMI software, that's running on data and you can, um, you know, fail a lot and bring the data onto the in-vitro Needham, VSO. It's, uh, uh, it's, uh, you know, and it's all there to look for SAS customer customer doesn't have to really manage anything on prem fuel, >>Which must've been a huge advantage in the last year and a half when it was so hard to get to the on-prem locations. Right. >>That's exactly right. And this is one of the clear differentiators, you know, against, uh, you know, with, um, uh, compared to the legacy systems, because in legacy backup and disaster recovery systems, you need to manage your, not just your target tourists, but also, you know, the Asians and, you know, all the stuff that, uh, uh, all the software that goes along with that, uh, data production and, uh, and the disaster recovery solution. And so by T and Matt upgrades and patches and so on. And so what we do with, with a SAS based approach is take away that burden away from customer. So we deliver this entire service as a SAS first as a cloud service first, um, uh, delivery mechanisms of customers are don't have water. You don't have to whatever any of those things. >>And that's critical, especially as we've seen in the last 18 months with what's been going on the challenge of getting to locations, but also what's been happening as we talked about in the cybersecurity space, on the increase, the massive increase in ransomware. Talk to me a little bit about, I want to dig in before we go about some of the ways that you've simplified and integrated the way to backup VMware cloud on AWS. Talk to me a little bit more about some of those enhancements specifically. Yeah, >>Yeah. So, um, a lot of the customers, customers, as you know, are, uh, you know, have a dual pronged approach where they have, you know, some workloads running on prem and they have some workloads running and the VMware cloud on AWS and for BNB, uh, for VMs that are running on VMware cloud on AWS. Um, you know, now they have a choice of, uh, of protecting, protecting the data and the VM very simply, uh, using the McLaurin disaster cloud disaster recovery. And what that means is that they don't need to have the full band BR solution, but they can simply protect the data and automatically restore and recover of data. If they, you know, if there's a corruption or something goes wrong with their, uh, you know, the beans, they can simply restore the data without going through an entire field processes. So we provide a simplified way for customers to automatically protect data, and then that are running on VMware cloud on AWS. And that's a, and it's fully integrated with our cloud on AWS, you know, workflows. And, um, and so that's a great win for anyone who's, who's migrating data man workloads into BMC >>Is the primary objective of that to deliver a business resiliency. Dr. >>Both actually that's, that's, that's, that's a great part about that. You know, that's a bit part of the solution is that customers don't have to choose between Dr and business resiliency. They get both with a single solution. They can start off, it's a specific business resiliency and protecting the data, but if they choose to, they can them, uh, you know, add BR as well to that, to those workflows. And so it's not either, or it's both. >>Excellent. Got it. Any other enhancements that you guys are announcing at the Emerald this year? >>Yeah. I just want to reiterate the announcements and the key enhancements and the making, making, uh, you know, the balancing beam. Well, um, the first one, as I said is, uh, uh, is 30 minutes RPO. So customers that are business critical workloads can now pro protect the data and be guaranteed that they're, you know, the, the, you know, the demo data, the data that they, um, you know, they lag behind it's, it's in the 30 minute range and not in the other screens, like with other legacy backup solutions. That's one. The second is the integration, uh, as all enhancements that, you know, that I just talked about for ransom recovery, single file, thin file restore. Um, they always had, you know, number of snapshots and, you know, failure was and so on, but silverish was a key and that's what they've been making for a ransomware recovery. And the third one is the integration with BNB coordinator. So the fully integrated solution and provides a simple, you know, sort of plug and play solution for any workload that's funding in being AWS. Those are the three Tiki announcements. There's a lot more in, um, in the world. So you'll see that in the coming weeks and months, but these are the three on to get the input, >>A lot of enhancements to a solution that was launched just about a year ago. VJ, thank you for sharing with us. What's new with VMware cloud DVR, the enhancements, what you're doing, and also how it's enabling customers to recover from that ever pressing, increasing threat of ransomware. We appreciate your thoughts and likewise for VJ Ramachandra and I'm Lisa Martin, you're watching the cubes coverage of VMworld 2021.

>> Narrator: From around the globe, it's theCUBE with digital coverage of Women Transforming Technology. Brought to you by VMware. >> Welcome to theCUBE, I'm Lisa Martin covering the fifth annual Women Transforming Technology. The first year that this event has gone completely digital. We're very pleased to welcome back to theCUBE one of our favorite alumni, the Chief People Officer of VMware, Betsy Sutter. Betsy, welcome back! >> Oh, thank you, Lisa. It's great to see you and it's great to be back. Love this time of year. >> Likewise, me too. And you know, I've had the great opportunity and pleasure of covering WT2 for theCUBE the last few years so I know walking into that courtyard area in Palo Alto, VMware's headquarters, you feel the energy and the excitement, and it's really genuine. And so, knowing that you had to pivot a couple you know, eight weeks or so ago or more, to convert what is such an engaging in-person experience to digital, hard decision, the right decision, but huge in terms of the number of attendees. Tell us a little bit about that process of taking We Rise digital. >> Yeah, you know, it was a pretty quick decision. At VMWare, we were starting to virtualize some other events, and so in realtime, we said, "let's go ahead "and virtualize Women Transforming Technology 2020." And so, when we immediate, flipped to that mode, things started to really open up. The possibilities became pretty interesting. And so honestly, we did not imagine you know, the people attending would grow from roughly thousands to over 5,000. And that's what digitalizing the event, virtualizing the event did. And it was super fun to use technology to make it so much more inclusive and accessible for people around the world. I'm sure you've heard that we had over 5,000 people from over 500 companies represented from 30 different countries. So that was amazing in its own right. >> One of the things that I think was a great advantage knowing that this was the fifth one, but that you had the opportunity to build the community, and such a strong, tight-knight community over the last few years, I think was probably a great facilitator of the event being so much bigger digitally. But when I spoke with a number of your speakers, everybody said, and I saw the Twitter stream, that the engagement, it wasn't like they were watching a video. It was really interactive, and that is hard to achieve with digital. >> Yeah, you know, what I love about the technology was that there were chat rooms, and there were Q&A rooms. And so, there was a lot of back and forth in realtime, even while the speakers were talking. You could sort of multitask, and the speakers were really, really fun to interact with that way as well. And it's super fun to see people in their home environments. You know, it's a just a little bit more information about them, and they seem a little bit more relaxed too, so it was tremendous. Watching Laura Dern, who is an activist and an obviously a very famous actress, in her own home talking to us about the issues she's faced as a woman in her industry, and then moving to another woman named Kathryn Finney, who is the CEO of digitalundivided, in her home with all the activity, she had a four-year old sort of in the background, was super fun and really landed their conversations with us even more solidly. It was a great day. >> I heard that throughout Twitter that people really felt that there was a personal connection. Lot of people talking about, I'm sitting here zooming with Laura Dern, what are you doing today? And some of the things that she said about, you know, you don't have to stay in your own swimlane. That resonated with me and I think with your community very well. >> You know,the diversity, the eclecticness of the women that were able to join from around the world and from many different industries, but you know, technical women, women in tech, was, it just up-leveled everything and it fit into the theme of the conference which was "We Rise", because you know, you're trying to rise as an individual, but there we were rising as a collective for a full day, and the workshops were super fun. I mean I participated in a number of 'em, and I literally went through a workshop with I don't know how many women, but you know, I was drawing on paper then engaging on the screen, then chatting, using the Q&A feature. It was a really dynamic day. I'm wondering now if we'll ever go back, honestly. >> Right, well I was already thinking, "Wow, you can take WT to global and do original events." And there's so much opportunity right now. Tremendous amount of challenge but on the same time, there is a lot of opportunity. In fact, when I was speaking with Sharmain (mumbles) yesterday, it was amazing that she was talking about, you know, right now, like the percentage increase, in people actually reading email because they have more time to, the commute time is gone. And so her advice to be really vivid, in making yourself visual, in terms of how you communicate, and evaluate your role and how you can add new value during this challenging time and I thought that was such a powerful message because we do need to look at what opportunities are we going to be able to uncover? There will be certain things that will go away, to your point, maybe we do digital because we can engage, we can interact and we can reach a bigger audience and learn from more people. >> Yeah, I think that's spot on. I couldn't have said that better. And you could really feel it that day and then the response from both the attendees, but even the keynote speakers, both Laura and Kathryn reaching back to us and talking about the experience they had. It was a pretty uplifting day, I'm still flying pretty high from it. And it was Cinco de Mayo so there had to have been at least margaritas, skinny margaritas, maybe, you know, virgin margaritas. But something there to celebrate an accomplishment of doing something in a short period of undertaking that community and being able to push the energy through the screen is awesome. I'd love to understand, you've been the Chief People Officer at the VMware for a while, the COVID crisis is so challenging in every aspect of life. We often talk about disruption, you know, in technology, a technology disruptor, you know, video streaming was a technology disruptor and Uber was a disruptor to transportation and the taxi service, but now the disruption is an unseen, scary thing and so the emotional impact, people are talking and a number of your folks I spoke to as well said it's hard to be motivated but it's important to acknowledge that I don't feel so motivated today for managers to be able to have that check-in with our employees and our teams. Tell me a little bit about the culture of VMware and how maybe the "We Rise" theme is really kind of, pervasive across VMware right now. >> Yeah, you know, one of the things that I believe and that I've seen in the people business is that more and more people join communities, they join companies but they join communities and communities come together based on you know, their actions, their ideas, their behaviors and what I've seen in terms of VMware's response to COVID-19 has been pretty remarkable. I think at first, you know, we were in crisis mode, sort of going in triage mode about what we do to keep our people feeling safe and healthy. But now we're sort of in a mode of "okay, there's a lot of opportunity that this presents." Now, we are very very fortunate, very blessed to be in the industry that we're in, and a lot of what we do and build and provide for our customers and partners fits into this new business model of working distributedly, so there's been some highs and some lows as we've navigated. First and foremost, we've just put our employees first and their health and safety, making sure that they're comfortable is just been top of mind for us. We just did a small sentiment survey, six questions. Because about two weeks ago, I realized, "I wonder if we really know how people are feeling about this?" And one of the things that came through, I'll say this, out of 32,000 people within 24 hours, over 10,000 people responded to this six question survey, they wanted to tell us how they were doing. But over 70% said they felt, if not the same amount of connection but more connection with each other working in a distributed fashion. And I think COVID-19's brought that alive. That we're going to work in a new way, it's a new business model and so we're doing it at VMware and then we're really pleased that we can offer that to our customers and partners around the globe. >> You know, I'm glad that you talked about the employee experience because obviously, with any business, customers are critical to the life, blood of that business. But equally important, if not sometimes more impactful to the revenue of an organization is the employee experience and being productive day in and day out. And that, if the employee experience is, I think, I don't know, you can't have a good customer experience without a good employee experience. And to (mumbles) that focus is key. So it must have been really nice for the VMware employees to go, "they're wanting to know how I feel right now." That's huge for people to know, the executive team genuinely cares. >> Yeah, you know, Lisa, we have really amped up our communications. We have done more town halls, whether it's to our management community our leadership and executive community or to the whole company. Yesterday alone, I think I did six town halls and two ask-me-anythings just to make sure we know it's on top of people's minds, what's important to them and that's kind of the new normal. And it's so much easier, right? I'm not trying to get to places, I'm just kind of clicking on a button and I'm all of a sudden talking to the employees in India. And you know, when I talk to my colleagues in other industries, like, Beth Axelrod or Tracey Ballow, that are in the you know, the Marriott and the Air BnB industries, their challaneges are so different. And what they're facing in this short-term, in the medium term. VMware is in a position where we can really help these businesses and at the core of that is really, how well our employees are doing and so that's been our focus. >> One of the things that I also talked about yesterday with Jo Miller, the CEO of Be Leaderly, was the difference between a mentor and a sponsor. And I had never even understood that they were two different things until WT2. And so, I thought, you know, we all know about mentors, we talk about that all the time. But I, she was really, I think it's an important message for your audience and ours to understand the difference and she said, "people are often over-mentored and under-sponsored." And so I thought, well, "I want to understand VMware's culture of sponsorship." Tell me what's going on in that respect. >> Yeah, we're, well, I agree with everything that you said on the mentorship side and so what we've instituted on the mentorship side at VMware's reverse mentorship. So every executive at VMware has a reverse mentor, so that they can learn something that they might not be thinking about. And whether it's a reverse mentor who happens to be, if you're a man, who happens to be a woman, or if you want to engage with the under-represented minority, or if you just want to learn about the different aspect of the business, we're big on reverse mentoring. On the sponsorship side, we do do that. And that's a really important aspect to any company's culture if you're trying to cultivate talent. And sponsorship is really championship, right? And I know I champion a lot of people, a lot of the talent around the company and it's very different than maybe coaching, advicing, and interacting in that venue. It's more about, what's the right opportunity for this person? When I'm in the board room, or when I'm in the executive staff meeting, actually advocating for that person, and I'm fierce about that. Especially for women right now at VMware, and it's just important. And a lot of people are starting to adopt that mindset because there's a lot more power and influence in having sponsorship behind you than having mentorship. >> I completely agree. Are you saying that, you know, we often talk about the hard skills and then the soft skills. And I always think soft is the wrong word but I keep forgetting to look it up on the thesaurus to get a better word. Because right now, I think, more important than ever, looking at someone who might have all of the hard skills to be on this the track to the c-suite,  but the importance of authenticity and empathy, I think now are under a microscope. We talked a lot about that too with some of your guests, tell me little bit about those kinds of conversations, that came up during the interactive sessions with WT2. >> Yeah, well, you know, this is one of the blessings that's come out of COVID-19, and this pandemic is that people are starting to see, because everyone's impacted by this and not just in one way, but in multiple ways. So, there's really this once in a lifetime opportunity, at least as far as what I've seen in my lifetime, to seize this heightened level of compassion and empathy for all the people around you in terms of what we're doing. At WT2, I saw it a lot in terms of the quality of the conversations that were happening virtually and sometimes with the key notes and the guest speakers, with the audience, there was always a lead-in with compassion and empathy in terms of all of us. All of us, no matter where you are in the world, or no matter what you're doing, adjusting to what we're calling this new normal. And there's a new business normal but the new normal on the personal side I think is going to take a little bit longer, right? In terms of what people are managing. But in the business world, I think you know, people are starting to re-bound and rebuild, they're honing those skills, and they're going to be wiser and better because of it. But at the heart of it all is, as you said, a lot more compassion and empathy 'cause never before, have we all kind of gone through something quite so traumatic as COVID-19. >> Traumatic and surreal. And you know, we are all in this same storm and I think there's a level of comfort there, that I know I feel with knowing, okay, everyone is going to be feeling this rollercoaster at some point. Some days you're here, some days you're here. But we're all in this, whether you're, you know, in your role, or Pat Gelsinger or an individual contributor role, we're all in the same sea. Betsy, congratulations on a successful fifth WT2, first digital. I'm so glad the theCUBE and myself was able to participate digitally. It's always one of my favorite events every year and I look forward to seeing you again soon, which I soon will be digitally, but I look forward to it. >> Lisa, thank you so much and thanks for all of your sponsorship and mentorship with WT2 over the years too. Thank you. >> All right, you too. That was Betsy Sutter, I'm Lisa Martin. You're watching theCUBE's coverage of Women Transforming Technology 2. Thanks for watching, see you next time. (soft music)

>> Live from Las Vegas, it's theCUBE covering Informatica World 2019. Brought to you by Informatica. >> Welcome everyone, you are watching theCUBE. We are kicking off a two-day event here at Informatica World 2019 in Las Vegas. I'm your host, and I'm co-hosting along with John Furrier. It's great to have you. Great to be here. >> Great to see you again. >> So, Informatica is really sitting in the sweet spot of a fast-growing area of technology, cloud and big data. I want to ask you a big question. Where is the market? What do you see happening in this sweet spot area? >> Well we're here in Informatica World. I think it's our fourth Cube coverage. We've been following these guys since they've gone private two years ago in depth. Interesting changeover. They went private just like Michael Dell did with Dell Technologies. And then they went public in great performance. We said at that time, if they can go private with the product skills that they have in their senior leadership, they could do well. And they've been on the same trend line, which has been really positive data. Now data is the hottest thing on the planet. This is the theme of the industry. Data is everything. Machine learning needs data. Data feeds machine learning. Machine learning feeds AI. This is a core innovator. Now the challenge is on the enterprise side is that data is structured. It's in all these different databases. So in an enterprise, data's kind of has all these legacy structures and legacy systems. And the cloud for instance. Cloud is where SaaS wins. And SaaS winners like Zoom Communications, Air BNB, you name all those successful cloud data companies. Data's at the heart of their value proposition. And data is unencumbered. There's no restrictions. They use data, data as analysis. They look at customer behavior, AB testing. So data is the heart of innovation. This is Informatica's plan here. CLAIRE is their AI product. Their theme is kind of clever. CLAIRE starts here. And this is really the focus for Informatica. Their opportunity is to be that independent vendor supplier, the Switzerland as it has been called, the neutral third party to bring data together On Premise and Cloud. That's what they're saying. That's their opportunity. The challenges are high. The data business is being regulated. We talk about it last time. You know, privacy, GDPR one-year anniversary, Microsoft's calling for more privacy. As more regulation comes in, that puts more restrictions on data. That requires more software. That creates overhead. Overhead is not good for SaaS business models. And that is where the conflict is. This is the opportunity, and if they can overcome that as a supplier, then they can do well. And data growth is just massive. Cloud, IoT Edge, you name it. Data is the center of the value proposition. >> Well, and we're going to have a lot of great guests on the program this week, in particular we're going to have Sally Jenkins talking about these four customer journeys that the customers are going on. And in fact data governance and privacy is one of the big tenants. So, they are making, they are saying this is our wheelhouse. We can do this. We can help you do this. >> Well, the thing is we're going to ask every guest the question of the week is What's the skill gaps? Because digital transformation although very relevant is only as good as the people and the culture that's behind it. And that's a theme that we hear all throughout our different CUBE events. If people have the culture for it, they could do it. DevOps is another word that has been kicked around. But ultimately if you don't have the people and just machines, it's really going to be a tough balance to strike. You need the machines, you need the data, you need the people. And this is where the challenge is in the industry. I think the skill gaps is a huge problem for digital transformation. It's to me the big blocker in seeing innovation accelerate. So customers are now having that journey. They're starting, they really think about how to architect their enterprise with an On Premise, with a Legacy and Cloud Native with full SaaS. And the companies that can get to a SaaS business model, managing the On-Premise's legacy will have a winning shot at taking new market share or top one down incumbents in leadership positions. >> I'm really excited about this idea. Asking people about the skill gap and where the next generation of jobs are going to be in big data. I saw a statistic, a survey from Google, 94% of IT managers can't find qualified candidates for open Cloud roles. That is-that's astonishing. I also saw an interesting quote from Tim Cook, who recently said that half of Apple's new hires are not going to have a college degree this year. He said when our own founder didn't have one. It kind of really shows you what you can do. >> It's really early. >> You might not need this degree. >> First of all, it's really, first of all I agree that degrees don't really matter. In some cases, old degrees might not apply to the new jobs. I'll give you an example. My daughter just graduated from Cal Berkeley this week. And they had the inaugural class of data, data science, data analytics. For the first time, first graduating class. That's a tell-sign that we're at the early, early stages. But data science can come from anyone. You could be, you know, anthropologist, you could be any any skill. You can solve a problem, you're good at math. You can see the big picture. You're seeing data science really becoming a career. And again, there's just not enough job openings. And data science isn't just for the data jockeys out there who just want to do data. There's cyber security, huge data-driven. Everything is data-driven. The big growth area in the enterprise is the IoT, the Edge. As devices come online for manufacturing to oil rigs to wind farms. The edge computing is a huge thing. And that's a data problem. Everything is a data problem. So this is where the industry is focused I think Informatica was really on it early. And now everyone's jumping in. You got Amazon, Google, Microsoft, the big cloud players, and you got all the existing incumbent enterprise suppliers all putting data at the center-value proposition. You know you got a lot of competition now for Informatica, and they have to make some good moves here. And what I'm going to be looking for here, Rebecca, is how they transform as a company. Because I think that they have to be an integration company. They want to be that Switzerland. They got to integrate to all the clouds. They got to integrate to all the different platforms and environments on the enterprise and create that one operating model. And this is something they say they want to do, and we're going to ask them. >> And you not only called them Switzerland, they've called themselves Switzerland. And so I think that they are. They do want that. They want that for themselves. They want they are having these partnerships with all of the major cloud providers. So, you said this is what you're going to be asking. This is what you're going to be looking for. What is it that you think will set them apart? >> I think ultimately I think Informatica's got a great management team when it comes to product and engineering. One of the things I've been impressed with is they get the product around data. The only thing I think that could be a headwind for them as a challenge is this regulatory environment. I brought that up earlier. I think this could be a challenge and an opportunity, and it could be the difference maker because there's no question that their value proposition or how they're dealing with data management, their deals we're going to hear about with the cloud and all of the new innovation they have with CLAIRE and AI. Certainly that's good. But if you don't have data-feeding machine learning, and the data's hard to get at, and it's regulated, you got clouds with geographies and countries have new regulations. This is a complicated problem. If they could create software to make that easier and create an abstraction layer and use the power of the cloud, I think they could have a winning formula. So to me, that's a killer opportunity. And then making data work for SaaS-oriented business models, On-Premise and in the cloud. >> I think you're absolutely right and we heard Anil Chakravarthy say this today. Data needs the machine learning an AI, AI machine learning need data. And any application of AI and machine learning is only as good as the data that's been collected. So, the other big challenge is what I think is going to be really exciting about for this show is seeing all of these use cases. In industry after industry we are seeing applications of AI and machine learning transforming business models and approaches and leadership and big ideas around these important game-changers in our industry. >> Yeah, one of the things that's interesting I had an interview with in the city of Howie Xu, who's formally VMWare engineer, entrepreneur, sold his company to Zscaler. He's an AI guy, and we talked about the SaaS business model. And one of the things that's key is if you don't have the data feeding the SaaS, it's not going to work, so to me if they could get that data back in to the system quicker with all that regulation, that's going to be a game changer. And I think they got to start thinking how they can show the customer proof points. That's going to be interesting when the customers start adapting in that scale. >> And as we've also said many times on theCUBE the governance is kind of a mess itself. I mean Washington doesn't quite know what to do with this and how to regulate it. How do you think that these technology companies should be working with Washington on this? >> Well that's a loaded question. First of all, I think the government is not the bellwether for technology innovation. In fact, I think innovation is stifled by too much regulation. There's got to have a balance there. One of the things that's positive is in the cyber-security area you see private, public partnerships go on where there's some joint sharing. I think cloud is going to be a catalyst. We're going to have the VP of marketing from Amazon web services on, I'm going to ask him that direct question. This is where the action is. So I think this notion of collaboration the enterprise and cloud players is going to be key because if you look at like just how search engines used to work back in the old days, if it was not encumbered by all this legacy infrastructure in the enterprise, it works great. The more you add complexity to things, the more you need software. The more you need software, you need horsepower to compute. You need more storage. So all these things are creating a different environment than it was just three years ago. So, you know can they adjust, can the industry shape itself out? I think the industry needs to lead here, not the government. >> What about the idea of Informatica working together with customers and making sure that they are in fact deriving value? Because I mean I think that's the other thing is that all of these companies know they need to have an AI strategy, they need to be using more machine learning. It's very complicated as you said. But then there's this question of am I really going to see a return of investment on this? >> Well, I think Informatica can do a good job working with cloud architecture and looking at because you got again IoT edge is coming around the corner. But if they can nail the architecture On-Premises and Cloud, that is a great start. The second thing that Informatica can help customers at, and this is a customer challenge, is where do you store the data? Because moving data around is very expensive. So this scenario is where you want it all on the cloud. This scenario is where you want it all On Premise. And this scenario is where you want it on both locations. And then with the edge, you want to move data I mean compute to where the data is. So, data becomes a very critical piece of the overall architecture and whoever can build this operating system's mindset will have a winning formula, and again being neutral is a critical strategy. And the more Informatica can help enterprise be more like consumer companies, the better. If you look at Slack for instance, it's an IPO candidate coming out very popular. It's just a chat kind of message board app. What made Slack successful is that they built connectors and APIs into all different tools. If Informatica could do that, that would be a winning formula because they want to be data brokering, they want to be data connecting, and they want to feed the applications and machine learning data. If they can't get data to the machine learning and AI, the AI will not be sufficient. And that will be a problem. >> Well, this is all the things we are going to be talking about over these next two days. John, I look forward to it. I'm Rebecca Knight, you are watching theCUBE. (lighthearted techno music)

(funky music) >> From our studios in the heart of Silicon Valley, Palo Alto, California, this is a CUBE conversation. >> Hey welcome back everybody, Jeff Rick here with theCUBE, we're having a CUBE conversation in the studio, we're just about ready to hit the crazy wave that is the conference season, so it's great to still have some time to do some studio stuff before we hit the road. We're excited to have a new guest who's never been on theCUBE before, he's Danial Hazariki, the CMO of Reflektive. Danial, great to meet you. >> Great to meet you. >> So you guys are working in a cool space, kind of the new age HR management for lack of a better term. We've had Patty Mccord on before, who obviously was seminal in kind of the Netflix culture, which I think was pretty early days in kind of saying throw out traditional annual reviews, kind of throw out regulations around expense reports, throw out a lot of these traditional mechanisms to manage people and really say what are we managing people to? And we should be giving them feedback on a regular basis and we really need to kind of bring this into the modern era. And that's smack in the middle of what you guys do. >> Absolutely yeah, a big part of what we do is managing employees to be high-performing, and that's the big tagline for her, is high-performance culture. I think it's critical to have, as part of that, a more active and ongoing role with your employees. That's why they can do things like remove expense report guidelines, because they know we're on the pulse of whether this person is actually performing or not, and by knowing that, we can have faith that we trust them, that they'll do the right thing when it comes to deciding on what they spend on. So, I think we sit right at the center of this, and we're really excited to be a part of it. >> So let's back up a little bit and just give everyone kind of the 411 on Reflektive. >> Absolutely. >> How many people are you, how long you been around, some of the basics. >> Yeah, so we were founded in late 2014, we have three co-founders, Rajeev Behera, Erick Tai, and Jimmie Tyrell. They more or less were actually people managers themselves, they realized that this was a gap in managing workforces, and, you know, classic model of technical founder, and then more of a product percent, and they got together and built this really cool tool. >> So what was the big hole? 'Cause there's a ton of HR applications out there. There's big ones like WorkDay, you know, who's been very successful on the SAAS model. What did they see that was the big hole, even though there's all these huge traditional HR applications? >> Absolutely, yeah, so what happened was there's a five-ish year old burst into framework, they talk about this. Systems and engagement, and systems of record. And so these tools that you mention, they were great at helping catalog what happens in a business, and do all the compliance processes required. But what happened was the world changed, things in terms of social media, the way people were getting information, the pace of things accelerated quite a bit, and these tools struggled to handle the day to day and didn't live where people worked, and those are big gaps. So they saw this and said okay well, there's something here where we can go and insert ourselves in the flow of people's work and help them actually get the information they need to be high-performing. >> So, was the entry point the annual review? What was kind of the entry point to get people to think about HR in a different way and to adopt the technology? >> Yeah, I think that ultimately, there is some form of review that happens, and they built that functionality. What was really interesting to the market was actually their concept of realtime feedback, and building the mechanisms by which you could actually bring that into that platform, and actually factor that in when you're doing interviews. This eliminates things like recency bias, things that, hey, a review is happening at the end of the year, I'm going to remember what happened the last three months. I'm not going to remember that you killed it in March of that year. So we're helping solve for that, and they saw great results doing that. >> Right, so you've got all types of little apps, is the right word, solutions, or kind of activities that enable people both as the employee as well as the manager as well as the HR people, to have kind of this ongoing back and forth relationship. So I wondered if you could dive into some of those applications and what's working really well that's different than things used to be? >> Yeah, so the modern kind of version of what we do, 'cause things have changed much over the past few years, we have a core kind of performance management offering, we also have an engagement offering, and we also have a people intelligence offering, and these are the three pillars by which we kind of enable all of those people that you just talked about. And so when we go back to the performance piece, there's many different components, but we believe that employees need feedback in the moment, they need a way to also do annual reviews. They need a way to set goals and be clear with their manager in what those are and what progress is. And we also believe that those things have to exist in the flow of day to day work, and that's why we do things like have a Slack integration, integrate with Gmail, Outlook, all these different kind of places where people actually live day to day. Then, you know, the other layers that I spoke about are engagement. We like to be able to do broad surveys to companies and get a pulse on high level, what is the emotion out there, how are people feeling about management? How are people feeling about even the snacks in the kitchen? Simple stuff like that. >> Right. >> And then, last but not least, all of that information has to feed into somewhere so that the management of an organization can get the insights they need to make decisions, and that's where the people intelligence comes in. >> Okay, so there's a lot of different layers to the story. But the one when I was first preparing for this interview, I'm like, oh my goodness, you were right, another tool, another desktop app, I forget what the statistics are of all the tabs that we have open with our sales force and Outlook and all these things are open. But you guys took an interesting approach, 'cause you actually integrated with some of the apps that you presume I have open like Slack, as opposed to kind of forcing me to have that one more tab. How does that work, and how has that kind of impacted adoption? >> Totally, yeah, I mean this is where the foundations of our company kind of come into play. So, our founders came from mobile applications, and games specifically. So they know how to optimize for things like active users daily, monthly, all that, right? And taking that lends to what they said. Okay, we really do need to encourage adoption, how do we make that happen? To your point, too many tools are open. Some are required to do your job, like email. Others are kind of optional. We're honest with ourselves, we say, hey, we're in the optional category, how do we solve for that? How do we still get people to use this? So we said okay, we're going to plug ourselves into Slack, where people actually communicate day to day. We're going to show up in Gmail, we're going to show up in Outlook, we're going to go to all these different places where people are already working. We actually even integrate with Jira, the engineering tool. And we said that's the way we'll actually get the information into our system that we need, and then we can service all those insights I talked about. >> Is it a popup, is it some encouragement when I do some activity, say, with you on a project? Oh Jeff, by the way, do you have any feedback for Danial? Oh Jeff, by the way, somebody's looking for feedback on Danial, how does the mechanics work, and then what have you seen in terms of adoption? What works and what doesn't work? >> Yeah, I mean it definitely gets traction, because I think specifically Slack, we're a Silicon Valley company, a lot of our earlier customers were Silicon Valley companies, and they all use Slack. >> It's as the way you said, very familiar. >> There you go right, so I think from that perspective, it's really easy to use. You can see all the active recognition, for example, happening in your company and in channel, you can also go and input recognition for other people, write there at mention, and just kind of invoke that. >> So are they kind of channels then within Slack around- >> Recognition can be a channel, but the actual input of feedback, it can do that right from the beginning of our, yeah. >> So interesting to talk about feedback versus recognition. How does that play out in the real world? 'Cause those are two very different words and two very different motivations. >> You bring up a great, great point, and it's an ongoing debate, how do you name these two different things? Frankly, recognition to the broader market ends up being, more or less, positive feedback that you feel like you want to put a public stamp on. But there's an important distinction here, because there's also negative feedback, and there's also just feedback that people want to give that's positive, but they don't necessarily want to share that with the entire world, or with a broader organization. So we wanted to create a safe space for them to be able to do that in every single use case, and so that's where the delineation between recognition and feedback comes in, is that you can go public, private, public and also broadcast to the whole company, and we wanted to give people the avenue to do all those things. >> Right, so I want to shift gears a little bit and talk about goals and goal management, and how does that kind of module work and or how does that tie back to some of the corporate goals and corporate initiatives? Can you tie it back to your Jira project and are these things integrated, or is it kind of a stand alone, and does it operate like an annual goal or a quarterly goal? How does that piece of it work? >> So the way we find the highest performance cultures doing this is they do kind of adjust goals on an ongoing basis. Ideally quarterly, I think that's kind of the favored happy medium right now. And that does start with company level goals. Then it goes to departmental, then it goes to individual, or sub team goals. And all of these people have, you can do smart goals, you can do objectives and key results, you can do whatever format you want, and it's pretty flexible as a platform. But all of that cascades down, and you can coordinate between people, and get visibility of public goals, private goals, and that's part of our whole commitment to transparency on the platform. >> And in terms of your customers and their adoption at a corporate level, not necessarily an individual, is it more of a stick or is it more of a carrot? Are people figuring out that they need to change, and yours is a tool to give them an avenue to the new way, or is it kind of new and provocative, and we've been doing annual reviews since my dad's dad's dad, I'm not quite sure about this ongoing thing. What's kind of the reception, and how is the market changing? >> Totally, like with anything, either tech adoption life cycle, a lot of our early adopters have just picked up on the fact that the market for talent is extremely competitive now, and some have gotten to different maturity levels in understanding what they need to do to deal with that. Our earliest adopters, they just got it right away. They said our workforce is asking for more in the moment feedback, they want to know what their goals are clearly, and be able to measure against them and be able to go and point back, hey, I actually achieved that, or I did not. And so that has helped us a lot with the earlier adopters, just saying we built something that's ideally suited to the way you need to evolve. Part of the task of any innovative technology is we have to go educate the market, too. We know that universally, people are struggling to attain talent, what we do to educate them is inform them of here's actually what the workforce is looking for. We've done a ton of research, HBR articles, we've seen gallop research, we've signed all sorts of stuff that tells you the world has changed, the workforce is expecting certain things, and we've built something around those needs. And so the more we do our job as marketing to make sure the market understands that, I think the more reflective we'll see success. >> That's funny, in one of Patty's recent medium posts, she talks about foosball tables, and billiard tables, it's like that's not what drives employee happiness and satisfaction. They look good, I guess, on the tour before you take the job, but a lot of other things, that drive, happiness and retention in the super competitive market that's not the ping pong table. >> Absolutely, especially in the case of Patty Mccord, I mean, she's indexing everything, again, around, you want to have the highest-performing people stay, and you don't necessarily care to actively manage the ones who are not. And what she has espoused many times is that the highest performing people actually love this. They love that there's transparency around the business value they're driving. They love to know exactly where they stand, they love to have feedback so they can improve and be better, and so you can see how there's a lot of parallels here about what she's talking about that high performing cultures do, and what the platform that we've built enables. >> Right, what about the pesky lawyers that are always saying there's always compliance issues, and we're still operating off of laws that were established before, and this is a little bit funky and we're not really sure how to deal with it. >> Yeah, what I've actually found is, there are specific customers, even of a size of Air BnB who will highlight that we helped them combat bias, and the way we do this, and evidence that they are not biased in the way they do reviews. And the way we do this, is I think ultimately, the concept of real time feedback. Because this stuff is being logged as it's happening, no one can say it's the end of the year now, and you just remember what happened in the past few months. You're ignoring all my great work that happened before that. This is not fair, that recency bias they call is eliminated. And that actually, in the end, helps with the lawyers, because we can say this was all cataloged in the moment as opposed to way later. >> Right, we have to train among contract year concept, you're supposed to turn it up the last month so they forget about the crappy stuff you did earlier in the year and do well. So Dan, before I let you go, you've been around a little while, you've been in the valley, you've been at a number of startups, you've been here for about a year, I'm just curious kind of as you've come to Reflektive and been there now. What was the biggest surprise entering this company that you didn't necessarily expect now that you've been there for a little bit? >> Yeah, I think what was most interesting and actually kind of exciting was to observe how similar the transformation that HR is going through right now is to the transformation that marketing went through 10 years ago. I'm seeing the movement to being more data driven to getting active information on how campaigns are running, all this stuff. That evolution is happening in HR right now, I'm seeing more and more people scientists, I'm seeing more and more people who are turning people management into a science, and I think a lot of it has to do with record low unemployment. The market for labor got so competitive that people have really started paying attention to this as a problem and trying to understand better outside of just simple compliance things. How can we actually actively manage our workforce into being high performing and happier? And that's really interesting for me. >> Awesome, well thanks for taking a few minutes out of your day and sharing the story. >> Absolutely. >> All right. He's Danial, I'm Jeff, you're watching theCUBE. We're having a CUBE conversation in our Palo Alto studios, we'll see you next time, thanks for watching. (funky music)

(energetic music) >> Live from Orlando Florida, it's theCUBE, covering Microsoft Ignite. Brought to you by Cohesity, and theCUBE's ecosystem partners. >> Welcome back everyone, to theCUBE's live coverage of Microsoft Ignite here in Orlando, Florida. I'm your host, Rebecca Knight, along with my cohost, Stu Miniman. We're joined by Lynn Lucas. She is the CMO of Cohesity. Thanks so much for coming on the program, Lynn. >> Oh, just so excited to be here with you guys and host you in the Cohesity booth for the first time at Microsoft Ignite. >> It's been a lot of fun. There's a lot of buzz around here, and it's fun to be right, to be your neighbor. Exactly. >> Great. >> So today, there's been a lot of news, some new exciting announcements of integrations with Microsoft. I wonder if you can walk our viewers a little bit through what Cohesity announced today. >> Absolutely. So, we have been partners with Microsoft for some time, and today, we announced extensions to our capabilities with Microsoft Azure and Office 365. So Cohesity now extends data protection and backup for Office 365, including granular recovery of mailboxes and granular search for discovery purposes. We also have extended our integration with the Azure data box, and we also are increasing our DR capabilities for our customers with Azure so we now have fail back from the Azure Cloud for disaster recovery purposes. So, just continuing to see tremendous growth, hundreds of Microsoft customers with Cohesity, and these new capabilities are going to expand the possibilities for them. >> Lynn, it's an interesting conversation these days 'cause, you know, in our research, and we've talked about this, data's at the center of everything, and the challenge for customers is data's everywhere. You look here at the Microsoft show, well, I've got all my traditional stuff, I've got my SaaS stuff, my PubliCloud stuff, now Edge with the data box things there. Microsoft plays across there, and it sounds like Cohesity is playing in all of these areas, too. >> Absolutely, and I thought, you know, Sacha did such a good job in the keynote yesterday of really laying out the imperative for digital transformation, data being at the heart of it, but also laying out one of the key challenges which he pointed out, which is the data silos. And, I think Cohesity is right smack in the center of that conversation because we've always been about consolidating secondary data silos. And, you know, our partnership with Microsoft, really, I think, reinforces what they've been talking about, which is also a hybrid strategy that the bulk of customers that we talk to see that their data is going to be on premise, it's going to be in the cloud, and increasingly, it's goinna at the Edge, and we span all of those locations to create this one operating environment so that things like the new open data initiative, I think, will be much easier for customers because they won't be wondering, well, is my data all in one place to be operated on? >> So, talk about the problem of the data silos, because, as you said, it's one of the biggest challenges that companies face today. They are data rich and yet, this data's here and this data's here. Can you describe a little bit about what kind of problems this is for companies, and why this matters? >> So, I think it's just something folks are starting to really get a handle on. As I talked to individual folks here at the show, you'd be surprised at how many aren't even really sure, maybe, how many islands they have, you know, so, even mapping where is all my data, I think, is a capability that many organizations are still getting their arms around. And the challenge, of course, is that in today's world, it's very expensive to move large data sets, and so you want to bring compute to the data, which is what a hyper-convergence in Cohesity is about. And, when you look at the imperatives at the board level, the CEO level, they increasingly see that data becomes really the true competitive advantage for most organizations, and yet, if they can't operate or bring compute to that data and do something with it, they're really at a handicap. We call, you know, some of the newer companies are kind of data-centric or data natives, the Air BNB's, the, maybe, Netflixes of the world, not everyone aspires to be them. As well, not everyone has the resources that those companies may have had or just stay short period of time. Most organizations have the benefit of years of data. We want to level the playing field and allow them to become competitive with their data by providing that single foundation. >> Yeah, Lynn, it's a big show here. They said thirty thousand people and a really diverse ecosystem. What really surprised me is the spectrum of customers that you have here. I mean, we know Microsoft has a long history in higher education. We spoke to one of your customers, Brown University, and of course, long history they have with Microsoft. What are some of the things that you're hearing from customers, maybe, what's different at this show than some of the other, cloud and kind of younger shows that we might go to. This show's been around about almost thirty years now, so. >> Yeah, you know, isn't it, you know, I hate to give our ages but, I think we've been doing this for a while now, right? And Microsoft has been part of the IT ecosystem in a major way, and it's great to see the vibrancy here and how they're talking about AI and ML and moving forward with it. You know, what strikes me here is that a lot of the organizations here are now really understanding the pragmatism of having a hybrid strategy of what makes sense in the cloud as well as what may continue to be on prem for them. I think we complement that well. I'm really excited, too, about the idea that we are going to be using machine learning to be doing a lot more that humans simply can't keep up with in terms of the data growth and then doing something productive with that. And I think that's a conversation that we're just tapping the surface of here at this show. >> Yeah, you've said something that really resonated with me. You know, we have people that have been in the industry a while and, I look at you, your founder, Mohit, and this isn't his first rodeo. He'd been looking at data back from a couple of generations of solutions, and people are very excited. Machine learning, as you said, we used to talk about automation and intelligence around this environment. Now, I lived in the storage industry for quite a while, and we've talked about it but it feels more real when I talk to the architects and the people building this stuff. They are just so excited about what we will be able to do today that we talked about a decade or so ago but now really can make reality for customers. >> No, absolutely, and I think, you know, we have our own investment in that. Helios, which we announced just last month, you know, provides that machine learning capability because what we hear from our customers is what they love is the ability to have simplicity because, let's face it, IT environments continue to grow in complexity. They're looking for ways to subtract that complexity so they can apply their talents to solving the primary mission, as I call it, of their organization, whether that be public sector or private sector, adoing that in a simpler way. You know, look, one of the great stories that one of our customers is talking about here is how Cohesity helped him with a standard thing that most IT organizations have, which is, we're going to do a power shut down and we've got to perform a DR failover, and this particular organization, University of Pennsylvania Annenberg, had a set of twelve websites which, the professors and the students rely on, and it was going to take them literally almost a month to try to move them, and they didn't have that kind of time, and with Cohesity, with our DR capabilities, he was able to do that literally with a few clicks, kept the community of professors and students happy, and didn't spend, more importantly, twenty days trying to rebuild websites for a standard IT event, right? That's the kind of real life story in terms of what IT gets back that they can invest in other more important focus areas for their business. >> Well, for their business and also, just for their lives giving people their time back, their weekends back, their time at night >> Weekends and nights, right? >> With their families, yeah. >> We all need that. >> Satya Nadella is such a proponent of an improving workplace productivity, even five percent, he says, can make this big difference. Can you talk a little bit about how you view that workplace productivity at Cohesity and your approach to giving people either time to concentrate on more value for their companies or just their lives? >> So, again, a super story that we have from another customer that is here at Microsoft, and is an Azure customer, and a Cohesity customer. HKS, one of the world's most respected architectural firms, designed AT&T Stadium, there's a new major pediatric hospital going in in Dubai. They operate in ninety-four countries with remote designers and architects, and because of their inefficient backup processes and archive processes, they literally were having their associates have to work weekends as well as losing time on their projects, and time is money, and they, you know, in some cases, are penalized if they don't make certain dates. And so, I think, these are really pragmatic examples. On average here, pulling some of the folks here, I've heard that they can get a day a week back, sometimes for their administrator who now doesn't have to do repetitive manual tasks anymore. >> One of the things we always love digging into is, you talk about people's jobs and some of the new careers that are happening. We talked to one guest earlier this week. He said, if you're a customer and you learn Azure as what you're doing, like, you're resume is gold. We've talked to, and the really early Edge, like site reliability engineering, he said, don't put SRE on your resume or every recruiter will be calling you up and you won't even be able to answer your phone. Cohesity, you're doing a bit of hiring also. Maybe you could talk about- >> We are! >> What are you seeing from customers and what are you looking for internally? >> We have tremendous good fortune, we grew three hundred percent in revenues year over year, we're hiring in our RTP offices, in our San Jose, in India, around the globe. You know, we look for the best and the brightest, a lot of engineering talent, marketing talent as well, really, across the board but, you know, I think to the point you just made for the IT folks that are here, looking forward as to how you are going to help your business with your data infrastructure or data flows throughout their organization is, to me, where some of the career movement is happening when you hear the talk about how important it is to so many aspects of the business. >> And what are the sort of challenges that you're having with hiring, or are you? I mean, you're a red hot company, but, are you finding it difficult to find the kind of skills, the kind of talent that you want? I mean, what is, what's the candidate pool like? >> You know, so, I think what's really interesting, we are red hot, we have a lot of applicants so, I'd say, in general, no, we're very blessed that way. I think, though, more businesses, including ours, are finding it's difficult to get, say, those data scientists, right? Some of these also front end or back end developers, you know, it's not just the technical companies that are recruiting for that anymore. It's not just the Cohesitys and the Microsofts that are looking for that talent, but it's now also the Netflixes or, you know, the eBays, et cetera, right? They are all looking for the type of talent that we are and so, in general, I think that this bodes well for young people or folks really anywhere in their career watching about, thinking about, where the talent needs are, and there's a lot of activity and interest in people with those kinds of skills. >> You know, let me just follow up on that. So, Cohesity is a Silicon Valley-based company but, as you mentioned, you've got an RTP location. We've seen quite a lot of Silicon Valley-based companies that are starting to do a lot more hiring outside 'cause it's, I'm going to be honest, really expensive to live in the valley these days. So, any commentary on that dynamic? >> Well, you know, I think you're in Boston, not the lowest cost market either in the country. >> True, it's true! >> Yeah, you know, I think with a lot of the technology that's out there, you know, people don't have to be co-located, and we certainly also look to develop and invest in other communities around the globe, so we're not looking solely in San Jose but also in RTP, we've got headquarters in Europe as well as, of course, in India. So we look for talent everywhere, and, my own personal team, you know, I have folks basically around the US as well as across parts of the globe because talent, in many cases, is what matters and where you are physically, you know, some of the great technology that's out there can help break down those barriers of time and distance. >> Finally, this conference, it's thirty thousand people from five thousand different companies around the world. What is going to be, I mean, we're only on day two, but, what's been your big take-away so far? What's the vibe you're getting here at Ignite? >> You know, the vibe has been one of energy, of excitement. I've talked to a lot of folks from around the globe. I've been actually, pretty amazed at some of the people from different countries around the globe that are here, which is fantastic to see that draw in, and I feel like there's a general sense of excitement that technology and what Microsoft's doing can help solve some of the bigger challenges that are here, in the world, and for their own businesses, and we really look forward to Cohesity helping them lay that great data infrastructure foundation, consolidate their silos and help them build a foundation for, you know, doing more with their data. >> Great. Lynn Lucas, thank you so much for coming on theCube. It was great, great talking to you. >> Thank you. >> I'm Rebecca Knight for Stu Miniman. We will have more from Microsoft Ignite and theCube's live coverage coming up in just a little bit. (electronic music)

>> Live from Boston, Massachusetts, it's the CUBE, covering IBM Chief Data Officer Summit, brought to you by IBM. (soft electronic music) >> Welcome to theCUBE's coverage of IBM Chief Data Strategy Officer Summit here in Boston, Massachusetts. I'm your host, Rebecca Knight, co-hosting here today with Dave Vellante. >> Hey, Rebecca. >> Great to be working with you again. >> Good to see you again. It's been a while. >> It has. >> Last summer, in the heat of New York. >> That's right, and now here we are in the dreariness of Boston. Dave, we just finished up the keynote. As you said, it's a meaty keynote. It's a seminal time for Chief Data Officers at companies. What did you hear? What most interested you about what Joe Kavanaugh said? >> Well, a couple things. I think it's worthwhile going back a few years. The ascendancy of the Chief Data Officer as a role and a title kind of emerged from the back-office records management side of the house. It really started in regulated industries. Financial services, healthcare, and government. For obvious reasons. These are data-oriented companies. They're highly regulated. There's a lot of risk. So, there's really sort of a risk-first approach. Then, that sort of coincided with the big data meme exploding. Then, this whole discussion of is data an asset or a liability? Increasingly, organizations are looking at it, as we know, as an asset. So, the Chief Data Officer has emerged as the individual who is responsible for the data architecture of the company, trying to figure out how to monetize data. Not necessarily monetize explicitly the data, but how data contributes to the monetization of the organization. That has a lot of ripple effects, Rebecca, in terms of technology implications, skillsets, obviously security, relationships with line of business, and fundamentally the organization and the mission of the company. So, IBM has been pretty leading and aggressive about going after the Chief Data Officer role, and has events like this, the Chief Data Officer Summit. They do them, kind of signature moments, and these little its and bit events. I don't know how many people you think are here. >> 150, I think. >> 150? Okay. And they're the data-rowdy of the Boston community. They're chartered with figuring out what the data strategy is. How to value data and how to put data front and center. Everybody talks about being a data-driven organization, but most organizations-- Everybody talks about becoming a digital business, but a digital business means that you are data driven. The data is first. You understand how to monetize data. You know how to value data. Your decisions are data-driven. I would say that less than 10% of the organizations that we work with are of that ilk. So, it's early days still. What was interesting about what Jim Kavanaugh says, they put forth this cognitive blueprint that Inderpal Bhandari, who'll be on theCUBE later, envisioned and has brought to life in his two years as the Chief Data Officer here at IBM. Now, what I like about what IBM is doing is they're sharing their dog food experience with their clients. He talked about that enterprise blueprint architecture but he also talked about what IBM is doing to transform. So, James Kavanaugh is the Senior Vice President of Transformation at IBM, and works directly for Jenny Remetti. He fundamentally talked about IBM as an organization that is data-first, cloud, and consumerization was the other big trend. Now, I don't know if IBM's hit on all three of those yet but they're certainly working to get there. The other thing that was interesting is they talked about the data warehouse as the former king, and now process is king. What I think is interesting about that, I want to explore this with those guys, is that technology largely is well known today. People have access to technology. You can get security from-- You can log in with Twitter linked in our Facebook. You can-- Look at Uber and Waze. They're really software companies but they're built on other platforms, like the cloud, for example. These horizontal platforms. It's the processes that are new and unknown. You know, when you look at these emerging companies like Air BnB and Uber and Waze, and so forth, the processes by which consumers interact with businesses are totally changed. >> Exactly. That is what Jim and James and Inderpal were saying is that this explosion in data is really forcing companies to rethink their business models. And it's-- Their reporting structures, how they innovate, the kinds of things that they're working on, the kinds of risks that are keeping them up at night. >> Yeah, Kavanaugh cited a study for 4,000 CXOs and they said the number one factor impacting business sustainability in the next five years are technology-related. Which again, I want to poke at that a little bit, because to me technology is not the problem. It's process and skill sets and people are the really big challenges. But, I think really what I interpret from that data, what the CXOs are saying, the challenge is applying technology to create a business capability that involves all the process changes, the organizational changes, the people and skills set issues. Of course, they threw in a little fear, uncertainty, and doubt with GDPR, the recent breaches. The other big thing that you hear from IBM at these events is that IBM is a steward of your data. That it's your data, we're not going to-- They have this notion of data responsibility. He didn't mention-- He said the unnamed west coast companies. Of course, he's talking about Google and Amazon, who are sucking in our data and then advertising to us and telling us, hey there's a special and what to buy and what movie to watch, and so forth. That's not IBM's business. But, there's a nuance there that again, I want to explore with these guys if we have time is, while IBM is not taking your data and then turning it into business through advertising, IBM is training models. I'm interested in hearing IBM's response about where's the dividing line between the model-- sorry, the data, and the model. If the data is informing the model, the model then becomes IP. What happens to that IP? Does it get shared across the client base within an industry? So, I really want to understand that better. >> Right, and that is one thing that Jim Kavanaugh will talk about, definitely, is the responsibility that IBM has in terms of our data and protecting it and keeping it private. >> Yeah, so what I like about these events is they're intimate. We get into it with the CDOs. We got CDOs at banks, we have the influencer panel coming on, a lot are data practitioners. And, so much has changed over the last three or four years that we're happy to be here with the CUBE. >> It is. It's going to be a great day. So, we will have much more here at the IBM Chief Data Officer Strategy Summit. I'm Rebecca Knight for Dave Vallante. Stay tuned. (soft electronic music)

>> Narrator: Live from Las Vegas it's The Cube covering Veritas Vision 2017. Brought to you by Veritas. >> Welcome back to Las Vegas, everybody this is The Cube, the leader in live tech coverage. We are here covering Veritas Vision 2017, the hashtag is VtasVision. My name is Dave Vellante, and I'm here with Stuart Miniman my cohost David Noy is here, he's the vice president of product management at Vertias. David, thanks for coming to The Cube. >> Thanks for having me, pretty excited. >> Yes, we enjoyed your keynote today taking us through the new product announcements. Let's unpack it, you're at the center of it all. Actually, let's start with the way you started your keynote is you recently left EMC, came here, why, why was that? >> I talk to lots and lots of customers, hundreds, thousands of customers. They're enterprise customers, they're all trying to solve the same kind of problems, reducing infrastructure costs, moving to commodity based architectures, moving to the cloud, in fact they did move to the cloud in Angara. If you look at the NAS market in 2016 it had been on a nice two percent incline until about the second half of 2016 it basically dove 12% and a big part of that was enterprises who were kicking the tires finally saying we're going to move to cloud and actually doing it as opposed to just talking about it. At EMC and a lot of the other big iron vendors they have a strategy that they discuss around helping customers move to cloud, helping them adopt commodity, but the reality is they make their money, their big margin points, on selling branded boxes, right? And as much as it's lip service, it's really hard to fulfill that promise when that's where you're making your revenue, you have revenue margin targets. Veritas on the other hand, it's a software company. We're here to sell software, we're able to make your data more manageable to understand that it's a truth in information, I don't need to own every bit, and I thought that the company that can basically A, provide the real promise of what software define offers is going to be a software company. Number two is that you can't buck the trend of the cloud it's going to happen, and either you're in the critical path and trying to provide friction, in which case you're going to become irrelevant pretty soon or you enable it and figure out how to partner with the cloud vendors in a nonthreatening way. I found that Veritas, because of its heterogeneity background, hey you want AIX, you want Linux, you want Solaris, great, we'll help you with all those. We can do the same thing with the cloud, and the cloud vendors will partner up with us because they love us for that reason. >> Before we get into the products, let's unpack that a little bit. Why is it that as Veritas you can participate in profit from that cloud migration? We know why you can't as a hardware vendor because ultimately the cloud vendor is going to be providing the box. >> Well, the answer is that, a couple things. One is, we believe and even the cloud vendors believe that you're going to be in a hybrid environment. If you project out for the next ten years, it's likely that a lot of data and applications and workloads will move to cloud, but not all of them will. And you probably end up in about a 50/50 shift. The vendor who can provide the management and intelligence and compliance capabilities, and the data protection capabilities across both your on-prem, and your off-premise state as a single unified product set is going to win, in my opinion, that's number one. Number two is that the cloud vendors are all great, but they specialize in different things. Some are specialized in machine learning, some are really good with visual image recognition, some are really good with mobile applications, and people are, in my opinion, going to go to two, three, four different clouds, just like I would go to contracting agencies, some might be good at giving me engineers, I might go to dice.com for engineers, I might go to something completely different for finance people, and you're going to use the best of breed clouds for specific applications. Being able to actually aggregate what you have in your universe of multicloud, and your hybrid environment and allowing you, as an administrator to be aware of all my assets, is something that as a non-branded box pusher, as a software vendor I can go do with credibility. >> You're a recovering box pusher. >> I'm a recovering box pusher, I'm one month into recovery, so thank you very much. >> And David, one of the things we're trying to understand a little bit, you've got products that live in lots of these environments, why do you have visibility into the data? Is it because they're backup customers, is it other pieces? Help us understand in that multicloud world, what I need to be to get that full. >> That's a great question and I'll bridge into some of the new products too. Number one is that Veritas has a huge amount of data that's basically trapped in repositories because we do provide backup, we're the largest backup vendor. So we have all this data that's essentially sitting inactive you know, Mike talks about it, Mike Palmer our CPO, talks about it as kind of like the Uber, you know, what do you do with your car when it's not being used, or Air BnB if you will, what do you do with your home when it's not being used, is you potentially rent it out. You make it available for other purposes. With all this trapped data, there's tons of information that we can glean that enterprises have been grabbing for years and years and years. So that's number one, we're in a great position 'cause we hold a lot of that data. Now, we have products that have the capabilities through classification engines, through engines that are extending machine learning capabilities, to open that data up and actually figure out what's inside. Now we can do it with the backup products, but let's face it, data is stored in a number of different other modaliites, right? So there's blocked data that is sitting at the bottom of containerized private clouds, there are tons and tons of unstructured data sitting in NAS repositories, and growing off-prem, but actually on prem this object storage technology for the set it and forget it long term retention. All of that data has hidden information, all of it can be extracted for more value with our same classification engines that we can run against the net backup estate, we can basically take that and extend that into these new modalities, and actually have compelling products that are not just offering infrastructure, but that are actually offering infrastructure with the promise of making that data more valuable. Make sense? >> It does, I mean it's the holy grail of backup. For years it's been insurance, and insurance is a good business, don't get me wrong, but even when you think about information governance, through sarbanes-oxley and FRCP et cetera, it was always that desire to turn that corpus of data into something more valuable than just insurance, it feels like, like you're saying with automated classification and the machine learning AI, we're sort of at the cusp of that, but we've been disappointed so many times what gives you confidence that this time it'll stick? >> Look, there's some very straightforward things that are happening that you just cannot ignore. GDPR is one, there's a specific timeline, specific rules, specific regulatory requirements that have to be met. That one's a no brainer, and that will drive people to understand that, hey when they apply our policies against the data that they have they'll be able to extract value. That'll be one of many, but that's an extreme proof-point because there's no getting around it, there's no interpretation of that, and the date is a hard date. What we'll do is we'll look quickly at other verticals, we'll look at vertical specific data, whether its in data surveillance, or germain sequencing or what have you, and we'll look at what we can extract there, and we'll partner with ISVs, is a strategy that I learned in my past life, in order to actually bring to market systems or solutions that can categorize specific, vertical industry data to provide value back to the end users. If we just try to provide a blanket, hey, I'm just going to provide data categorization, it's a swiss army knife solution. If we get hyper-focused around specific use cases, workloads and industries now we can be very targeted to what the end users care about. >> If I heard right, it's not just for backup, it's primary and secondary data that you're helping to solve and leverage and put intelligence into these products. >> That's right, initially we have an enormous trapped pool of secondary data, so that's great, we want to turn that trapped pool from just basically a stagnant pool into something that you can actually get value out of. >> That Walking Dead analogy you used. >> The Walking Dead, yeah. We also say that there's a lot of data that sits in primary storage, in fact there's a huge category of archive, which we call active archive, it's not really archive, still wanted on spinning disk or flash. You still want to use it for some purpose but what happens when that data goes out into the environment? I talked to customers in automotive, for example, automotive design manufacturers, they do simulations, and they're consuming storage and capacity all the time, they've got all of these runs, and they're overrunning their budget for storage and they have no idea which of those runs they can actually delete, so they create policies like "well, if it hasn't been touched "in 90 days, I'll delete it," Well, just because it hasn't been touched in 90 days doesn't mean there wasn't good information to be gleaned out of that particular simulation run, right? >> Alright, so I want to get back to the object, but before we go deeper there, block and file, there's market leaders out there that seems that, it's a bit entrenched, if you will, what between the hyperscale product and Veritas access, what's the opportunity that you see that Veritas has there, what differentiates you? >> Sure, well, let's start with block. The one big differentiator we'll have in block storage is that it's not just about providing storage to containerized applications. We want to be able to provide machine learning capabilities to where we can actually optimize the IO path for quality of service. Then, we also want to be able to through machine learning determine whether, if it's how you decide to run your business, you want a burst workloads actually out into the cloud. So we're partnered with the cloud vendors, who are happy to partner with us for the reasons that I described earlier, is that we're very vendor agnostic, we're very heterogeneous. To actually move workloads on-prem and off-prem that's a very differentiated capability. You see with a few of the vendors that are out there, I think Nutanix for example, can do that, but it's not something that everyone's going after, because they want to keep their workloads in their environments, they want to check controls. >> And if I can, that high speed data mover is your IP? >> That's right, that's our IP. Now, on the file system side... >> Just one thing, cloud bursting's one of those things, moving real-time is difficult, physics is still a challenge for us. Any specifics you can give, kind of a customer use case where they're doing that? A lot of times I want this piece of the application here, I want to store the data there, but real time, doing things, I can't move massive amounts of data just 'cause, speed of light. >> If you break it down, I don't think that we're going to solve the use case of, "I'm going to snap my finger "and move the workload immediately offline." Essentially what we'll do is we'll sync the data in the background, once it has been synced we'll actually be able to move the application offline and that'll all come down to one of two things: Either user cases that exceed the capabilities of the current infrastructure and I want to be able to continue to grow without building them into my data center, or I have an end of the month processing. A great case is I have a media entertainment company that I used to work with that was working on a film, and it came close to the release date of that film, and they were asked to go back and recut and reedit that film for specific reasons, a pretty interesting reason actually, it had to do with government pressure. And when they went to go back and edit that film they essentially had a point where like, oh my gosh, all of the servers that were dedicated to render for this film have been moved off to another project. What do we do now, right? The answer is, you got to burst. And if you had cloud burst capabilities you could actually use whatever application and then containerize whether you're running on-prem or off-prem, it doesn't matter, it's containeraized, if we can get the data out there into the cloud through fast pipes then basically you can now finish that job without having to take all those servers back, or repurchase that much infrastructure. So that's a pretty cool use case, that's things that people have been talking about doing but nobody's every successfully done. We're staring to prove that out with some vendors and some partners that potentially even want to embed this in their own solutions, larger technology partners. Now, you wanted to talk about file as well, right, and what makes file different. I spent five years with one of the most successful scale-up file systems, you probably know who they are. But the thing about them was that extracting that file system out of the box and making it available as a software solution that you could layer on any hardware is really hard, because you become so addicted to the way that the behavior of the underlying infrastructure, the behavior of the drives, down to the smart errors that come off the drives, you're so tied into that, which is great because you build a very high performance available product when you do that, but the moment you try to go to any sort of commodity hardware, suddenly things start to fall apart. We can do that, and in fact with our file system we're not saying "hey, you've got to go it on "commodity servers and with DAS drives in them." You could layer it on top of your existing net app, your Isolon, your whatever, you name it, your BNX, encapsulate it, and create policies to move data back and forth between those systems, or potentially even provision them out say, "okay, you know what, this is my gold tier, "my silver tier, my bronze tier." We can even encapsulate, for example, a directory on one file service, like a one file system array, and we can actually migrate that data into an object service, whether its on-prem or off-prem, and then provide the same NFS or SMB connectivity back into that data, for example a home directory migration use case, moving off of a NAS filer onto an object storer, on premise or off premise and to the end user, they don't know that things have actually moved. We think that kind of capability is really critical, because we love to sell boxes, if that's what the customer wants to buy from us, and appliance form factor, but we're not pushing the box as the ultimate end point. The ultimate end point is that software layer on top, and that's where the Veritas DNA really shines. >> That's interesting, the traditional use cases for block certainly, and maybe to a lesser extent file, historically fairly well known an understood. So to your point, you could tune an array specifically for those use cases, but in this day and age the processes, and the new business models that are emerging in the digital economy, very unpredictable in terms of the infrastructure requirements. So your argument is a true software defined capability is going to allow you to adapt much more freely and quickly. >> We've also built and we've demoed at Vision this week machine learning capabilities to actually go in and look at your workloads that are running against those underlying infrastructure and tell you are they correctly positioned or not. Oh, guess what, we really don't think this workload should belong on this particular tier that you've chosen, maybe you ought to consider moving it over here. That's something that historically has been the responsibility of the admin, to go in and figure out where those policies are, and try to make some intelligent decisions. But usually those decisions are not super intelligent, they're just like, is it old, is it not old, do I think it's going to be fast? But I don't really know until runtime, based on actual access patterns whether it's going to be high performance or not. Whether it's going to require moving or aging or not. By using machine learning type of algorithms we can actually look at the data, the access patterns over time, and help the administrators make that decision. >> Okay, we're out of time, but just to summarize, hyperscales, the block, access is the scale out, NAS piece, cloud object... >> Veritas cloud storage we call it. Veritas cloud storage, very similar to the access product is for object storage, but again it's not trying to own the entire object bits, if you will, we'll happily be the broker and the asset manager for those objects, classify them and maintain the metadata catalog, because we think it's the metadata around the data that's critical, whether it lives off-prem, on-prem, or in our own appliance. >> You had a nice X/Y graph, dollars on the vertical axis, high frequency of access to the left part of the horizontal axis, lower SLAs to the right, and you had sort of block, file, object as the way to look at the world. Then you talked about the intelligence you bring to the object world. Last question, and then let's end there. Thoughts on object, Stu and I were talking off camera, it's taken a long time, obviously S3 and the cloud guys have been there, you've seen some take outs of object storage companies. But it really hasn't exploded, but it feels like we're on the cusp. What's your observation about object? >> I think object is absolutely on the cusp. Look, people have put it on the cloud, because traditionally object has been used for keeping deep, and because performance doesn't matter, and the deeper you get, the less expensive it gets. So a cloud provider's great, because they're going to aggrigate capacity across 1,000 or 20,000 or a million customers. They can get as deep as possible, and they can slice it off to you. As a single enterprise, I can never get as deep as a cloud service provider. >> The volume, right? >> But what ends up happening is that more and more workloads are not expecting to hold a connection open to their data source. They're actually looking at packetize, get-put type semantics that you can see in genomic sequencing, you see it in a number of different workloads where that kind of semantic, even in hydoop analytic workloads, where that kind of get-put semantic makes sense, not holding that connection open, and object's perfect for that, but it hasn't traditionally had the performance to be able to do that really well. We think that by providing a high performance object system that also has the intelligence to do that data classification, ties into our data protection products, provides the actionable information and metadata, and also makes it possible to use on-prem infrastructure as well as push to cloud or multicloud, and maintain that single pane of glass for that asset management for the objects is really critical, and again, it's the software that matters, the intelligence we build into it that matters. And I think that the primary workloads in a number of different industries in verticals or in adopting object more and more, and that's going to drive more on premise growth of object. By the way, if you look at the NAS market and the object market, you see the NAS market kind of doing this, and you see the object market kind of doing this, it's left pocket right pocket. >> And that get-put framework is a simplifying factor for organizations so, excellent. David, thank you very much for coming on The Cube. We appreciate it. >> Appreciate it, thanks for having me. >> You're welcome, alright, bringing you the truth from Veritas Visions, this is The Cube. We'll be right back, right after this short break.

(upbeat music) >> Hi we're at the Palo Alto studio of SiliconANGLE Media and theCUBE. My name is George Gilbert, we have a special guest with us this week, Veeru Ramaswamy who is VP IBM Watson IoT platform and he's here to fill us in on the incredible amount of innovation and growth that's going on in that sector of the world and we're going to talk more broadly about IoT and digital twins as a broad new construct that we're seeing in how to build enterprise systems. So Veeru, good to have you. Why don't you introduce yourself and tell us a little bit about your background. >> Thanks George, thanks for having me. I've been in the technology space for a long time and if you look at what's happening in the IoT, in the digital space, it's pretty interesting the amount of growth, the amount of productivity and efficiency the companies are trying to achieve. It is just phenomenal and I think we're now turning off the hype cycle and getting into real actions in a lot of businesses. Prior to joining IBM, I was junior offiicer and senior VP of data science with Cable Vision where I led the data strategy for the entire company and prior to that I was the GE of one of the first two guys who actually built the Cyamon digital center. GE digital center, it's a center of excellence. Looking at different kinds of IoT related projects and products along with leading some of the UX and the analytics and the club ration or the social integration. So that's the background. >> So just to set context 'cause this is as we were talking before, there was another era when Steve Jobs was talking about the next work station and he talked about objectory imitation and then everything was sprinkled with fairy dust about objects. So help us distinguish between IoT and digital twins which GE was brilliant in marketing 'cause that concept everyone could grasp. Help us understand where they fit. >> The idea of digital twin is, how do you abstract the actual physical entity out there in the world, and create an object model out of it. So it's very similar in that sense, what happened in the 90s for Steve Jobs and if you look at that object abstraction, is what is now happening in the digital twin space from the IoT angle. The way we look at IoT is we look at every center which is out there which can actually produce a metric on every device which produces a metric we consider as a sense so it could be as simple as the pressure, temperature, humidity sensors or it could be as complicated as cardio sensors and your healthcare and so on and so forth. The concept of bringing these sensors into the to the digital world, the data from that physical world to the digital world is what is making it even more abstract from a programming perspective. >> Help us understand, so it sounds like we're going to have these fire hoses of data. How do we organize that into something that someone who's going to work on that data, someone is going to program to it. How do they make sense out of it the way a normal person looks at a physical object? >> That's a great question. We're looking at sensors as a device that we can measure out of and that we call it a device twin. Taking the data that's coming from the device, we call that as a device twin and then your physical asset, the physical thing itself, which could be elevators, jet engines anything, physical asset that we have what we call the asset twin and there's hierarchical model that we believe that will have to be existing for the digital twin to be actually constructed from an IoT perspective. The asset twins will basically encompass some of the device twins and then we actually take that and represent the digital twin on a physical world of that particular asset. >> So that would be sort of like as we were talking about earlier like an elevator might be the asset but the devices within it might be the bricks and the pulleys and the panels for operating it. >> Veeru: Exactly. >> And it's then the hierarchy of these or in manufacturing terms, the building materials that becomes a critical part of the twin. What are some other components of this digital twin? >> When we talk about digital twin, we don't just take the blueprint as schematics. We also think about the system, the process, the operation that goes along with that physical asset and when we capture that and be able to model that, in the digital world, then that gives you the ability to do a lot of things where you don't have to do it in the physical world. For instance, you don't have to train your people but on the physical world, if it is periodical systems and so on and so forth, you could actually train them in the digital world and then be able to allow them to operate on the physical world whenever it's needed. Or if you want to increase your productivity or efficiency doing predictive models and so forth, you can test all the models in your digital world and then you actually deploy it in your physical world. >> That's great for context setting. How would you think of, this digital twins is more than just a representation of the structure, but it's also got the behavior in there. So in a sense it's a sensor and an actuator in that you could program the real world. What would that look like? What things can you do with that sort of approach? >> So when you actually have the data coming this humongous amount of terabyte data that comes from the sensors, once you model it and you get the insights out of that, based on the insight, you can take an actionable outcome that could be turning off an actuator or turning on an actuator and simple thngs like in the elevator case, open the door, shut the door, move the elevator up, move the elevator down etc. etc All of these things can be done from a digital world. That's where it makes a humongous difference. >> Okay, so it's a structured way of interacting with the highly structured world around us. >> Veeru: That's right. >> Okay, so it's not the narrow definition that many of us have been used to like an airplane engine or the autonomous driving capability of a car. It's more general than that. >> Yeah, it is more general than that. >> Now let's talk about having sort of set context with the definition so everyone knows we're talking about a broader sense that's going on. What are some of the business impacts in terms of operational efficiency, maybe just the first-order impact. But what about the ability to change products into more customizable services that have SLAs or entirely new business models including engineered order instead of make to stock. Tell us something about that hierarchy of value. >> That's a great question. You're talking about things like operations optimization and predicament and all of that which you can actually do from the digital world it's all on digital twin. You also can look into various kinds of business models now instead of a product, you can actually have a service out of the product and then be able to have different business models like powered by the hour, pay per use and kinds of things. So these kinds of models, business models can be tried out. Think about what's happening in the world of Air BnB and Uber, nobody owns any asset but still be able to make revenue by pay per use or power by the hour. I think that's an interesting model. I don't think it's being tested out so much in the physical asset world but I think that could be interesting model that you could actually try. >> One thing that I picked up at the Genius of Things event in Munich in February was that we really have to rethink about software markets in the sense that IBM's customers become in the way your channel, sometimes because they sell to their customers. Almost like a supply chain master or something similar and also pricing changes from potentially we've already migrated or are migrating from perpetual licenses to service softwares or service but now we could do unit pricing or SLA-based pricing, in which case you as a vendor have to start getting very smart about, you owe your customers the risk in meeting an SLA so it's almost more like insurance, actuarial modeling. >> Correct so the way we want think about is, how can we make our customers more, what do you call, monetizable. Their products to be monetizable with their customers and then in that case, when we enter into a service level agreement with our customers, there's always that risk of what we deliver to make their products and services more successful? There's always a risk component which we will have to work with the customers to make sure that combined model of what our customers are going to deliver is going to be more beneficial, more contributing to both bottom line and top line. >> That implies that your modeling, someone's modeling and risk from you the supplier to your customer as vendor to their customer. >> Right. >> That sounds tricky. >> I'm pretty sure we have a lot of financial risk modeling entered into our SLAs when we actually go to our customers. >> So that's a new business model for IBM, for IBM's sort of supply chain master type customers if that's the right word. As this capability, this technology pervades more industries, customers become software vendors or if not software vendors, services vendors for software enhanced products or service enhanced products. >> Exactly, exactly. >> Another thing, I'd listened to a briefing by IBM Global Services where they thought, ultimately, this might end up where there's far more industries are engineered to order instead of make to stock. How would this enable that? >> I think the way we want think about it is that most of the IoT based services will actually start by co-designing and co-developing with your customers. And that's where you're going to start. That's how you're going to start. You're not going to say, here's my 100 data centers and you bring your billion devices and connect and it's going to happen. We are going to start that way and then our customers are going to say, hey by the way, I have these used cases that we want to start doing, so that's why platform becomes so imortant. Once you have the platform, now you can scale, into a scale, individual silos as a vertical use case for them. We provide the platform and the use cases start driving on top of the platform. So the scale becomes much easier for the customers. >> So this sounds like the traditional application. The traditional way an application vendor might turn into a platform vendor which is a difficult transition in itself but you take a few use cases and then generalize into a platform. >> We call that a zone application services. The zone application service is basically, is drawing on perfectly cold platform service which actually provides you the abilities. So for instance like an asset management. An asset management can be done in an oil and gas rig, you can look at asset management in power tub vine, you can can look at asset management in a jet engine. You can do asset management across any different vertical but that is a common horizontal application so most of the time you get 80% of your asset management API's if you will. Then you can be able to scale across multiple different vertical applications and solutions. >> Hold that thought 'cause we're going to come back to joint development and leveraging expertise from vendor and customer and sharing that. Let's talk just at a high level one of the things that I keep hearing is that in Europe industry 4.0 is sort of the hot topic and in the states, it's more digital twins. Help parse that out for us. >> So the way we believe how digital twin should be viewed is a component view. What we mean the component view is that we have your knowledge graph representation of the real assets in the digital world and then you bring in your IoT sensors and connections to the models then you have your functional, logical, physical models that you want to bring into your knowledge graph and then you also want to be able to give the ability of search visualize allies. Kind of an intelligent experience for the end consumer and then you want to bring your similation models when you do the actual similation models in digital to bring it in there and then your enterprise asset management, your ERP systems, all of that and then when you connect, when you're able to build a knowledge graph, that's when the digital twin really connects with your enterprise systems. Sort of bring the OT and the IT together. >> So this is sort of to try and summarize 'cause there are a lot of moving parts in there. You've got you've got the product hierarchy which, in product Kaiser call it building materials, sort of the explosion of parts in an assembly, sub-assembly and then that provides like a structure, a data model then the machine learning models in the different types of models that they could be represent behavior and then when you put a knowledge graph across that structure and behavior, is that what makes it simulation ready? >> Yes, so you're talking about entities and connecting these entities with the actual relationship between these entities. That's the graph that holds the relation between nodes and your links. >> And then integrating the enterprise systems that maybe the lower level operation systems. That's how you effect business processes. >> Correct. >> For efficiency or optimization, automation. >> Yes, take a look at what you can do with like a shop floor optimization. You have all the building materials, you need to know from your existing ERP systems and then you will actually have the actual real parts that's coming to your shop floors to manage them and now base supposing, depending on whether you want to repair, you want to replace, you want an overall, you want to modify whatever that is, you want to look at your existing building materials and see, okay do I first have it do we need more? Do we need to order more? So your auditing system naturally gets integrated into that and then you have to integrate the data that's coming from these models and the availability of the existing assets with you. You can integrate it and say how fast can you actually start moving these out of your shop, into the. >> Okay that's where you translate essentially what's more like intelligent about an object or a rich object into sort of operational implications. >> Veeru: Yes. >> Okay operational process. Let's talk about customer engagement so far. There's intense interest in this. I remember in the Munich event, they were like they had to shut off attendance because they couldn't find a big enough venue. >> Veeru: That's true. >> So what are the characteristics of some of the most successful engagements or the ones that are promising. Maybe it's a little early to say successful. >> So, I think the way you can definitely see success from customer engagement are two fold. One is show what's possible. Show what's possible with after all desire to connect, collection of data, all of that so that one part of it. The second part is understand the customer. The customer has certain requirements in their existing processes and operations. Understand that and then deliver based on what solutions they are expecting, what applications they want to build. How you bring them together is what is, so we're thinking about. That Munich center you talked about. We are actually bringing in chip manufacturers, sensor manufacturers, device manufacturers. We are binging in network providers. We are bringing in SIs, system integrators all of them into the fold and show what is possible and then your partners enable you to get to market faster. That's how we see the engagement with customer should happen in a much more foster manner and show them what's possible. >> It sounds like in the chip industry Moore's law for many years it wasn't deterministic that you we would do double things every 18 months or two years, it was actually an incredibly complex ecosystem web where everyone's sort of product release cycles were synchronized so as to enable that. And it sounds like you're synchronizing the ecosystem to keep up. >> Exactly The saxel of a particular organization IoT efforts is going to depend on how do you build this ecosystem and how do you establish that ecosystem to get to market faster. That's going to be extremely key for all your integration efforts with your customer. >> Let's start narrowly with you. IBM what are the key skills that you feel you need to own starting from sort of the base rocket scientists you know who not only work on machine learning models but they come up with new algorithms on top of say tons of flow work or something like that. And all the way up to the guys who are going to work in conjunction with the customer to apply that science to a particular industry. How does that hold together? >> So it all starts on the platform. On the platform side we have all the developers, the engineers who build these platform all the video connection and all of that to make the connections. So you need the highest software development engineers to build these on the platform and then you also need the solution builders so who is in front of the customer understanding what kind of solutions you want to build. Solutions could be anything. It could be predictive maintenance, it could be as simple as management, it could be remote monitoring and diagnostics. It could be any of these solutions that you want to build and then the solution builders and the platform builders work together to make sure that it's the holistic approach for the customer at the final deployment. >> And how much is the solution builder typically in the early stages IBM or is there some expertise that the customer has to contribute almost like agile development, but not two programmers but like 500 and 500 from different companies. >> 500 is a bit too much. (laughs) I would say this is the concept of co-designing and co-development. We definitely want the ultimate, the developer, the engineers form, the subject exports from our customers and we also need our analytics experts and software developers to come and sit together and understand what's the use case. How do we actually bring in those optimized solution for the customer. >> What level of expertise or what type of expertise are the developers who are contributing to this effort in terms of do they have to, if you're working with manufacturing let's say auto manufacturing. Do they have to have automotive software development expertise or are they more generically analytics and the automotive customer brings in the specific industry expertise. >> It depends. In some cases we have RGB for instance. We have dedicated servers, that particular vertical service provider. We understand some of this industry knowledge. In some cases we don't, in some cases it actually comes from the customer. But it has to be an aggregation of the subject matter experts with our platform developers and solution developers sitting together, finding what's the solution. Literally going through, think about how we actually bring in the UX. What does a typical day of a persona look like? We always by the way believe it's an augmented allegiance which means the human and the machine work together rather than a complete. It gives you the answer for everything you ask for. >> It's a debate that keeps coming up Doug Anglebad sort of had his own answer like 50 years ago which was he sort of set the path for modern computing by saying we're not going to replace people, we're going to augment them and this is just a continuation of that. >> It's a continuation of that. >> Like UX design sounds like someone on the IBM side might be talking to the domain expert and the customer to say how does this workflow work. >> Exactly. So have this design thinking, design sessions with our customers and then based on that we take that knowledge, take it back, we build our mark ups, we build our wire frames, visual designs and the analytics and software that goes behind it and then we provide on top of platform. So most of the platform work, the standard what do you call table state connections, collection of data. All of that as they are already existing then it's one level above as to what the particular solution a customer wants. That's when we actually. >> In terms of getting the customer organization aligned to make this project successful, what are some of the different configurations? Who needs to be a sponsor? Where does budget typically come from? How long are the pilots? That sort of stuff so to set expectations. >> We believe in all the agile thinking, agile development and we believe in all of that. It's almost given now. So depending on where the customer comes from so the customer could actually directly come and sign up to our platform on the existing cloud infrastructure and then they will say, okay we want to build applications then there are some customers really big customers, large enterprises who want to say, give me the platform, we have our solution folks. We will want to work on board with you but we also want somebody who understands building solutions. We integrate with our solution developers and then we build on top of that. They build on top of that actually. So you have that model as well and then you have a GBS which actually does this, has been doing this for years, decades. >> George: Almost like from the silicon. >> All the way up to the application level. >> When the customer is not outsourcing completely, The custom app that they need to build in other words when when they need to go to GBS Global Business Services, whereas if they want a semi-packaged app, can they go to the industry solutions group? >> Yes. >> I assume it's the IoT, Industry Solutions Group. >> Solutions group, yes. >> They then take a it's almost maybe a framework or an existing application that needs customization. >> Exactly so we have IoT-4. IoT for manufacturing, IoT for retail, IoT for insurance IoT for you name it. We have all these industry solutions so there would be some amount of template which is already existing in some fashion so when GBS gets a request to say here is customer X coming and asking for a particular solution. They would come back to IoT solutions group to say, they already have some template solutions from where we can start from rather than building it from scratch. You speed to market again is much faster and then based on that, if it's something that is to be customizable, both of them work together with the customer and then make that happen, and they leverage our platform underneath to do all the connection collection data analytics and so on and so forth that goes along with that. >> Tell me this from everything we hear. There's a huge talent shortage. Tell me in which roles is there the greatest shortage and then how do different members of the ecosystem platform vendors, solution vendors sort of a supply-chain master customers and their customers. How do they attract and retain and train? >> It's a fantastic question. One of the difficulties both in the valley and everywhere across is that three is a skill gap. You want advanced data scientists you want advances machinery experts, you want advanced AI specialists to actually come in. Luckily for us, we have about 1000 data scientists and AI specialists distributed across the globe. >> When you say 1000 data scientists and AI specialists, help us understand which layer are they-- >> It could be all the way from like a BI person all the way to people who can build advanced AI models. >> On top of an engine or a framework. >> We have our Watson APIs from which we build then we have our data signs experience which actually has some of the models then built on top of what's in the data platform so we take that as well. There are many different ways by which we can actually bring the AM model missionary models to build. >> Where do you find those people? Not just the sort of band strengths that's been with IBM for years but to grow that skill space and then where are they also attracted to? >> It's a great question. The valley definitely has a lot of talent, then we also go outside. We have multiple centers of excellence in Israel, in India, in China. So we have multiple centers of excellence we gather from them. It's difficult to get all the talent just from US or just from one country so it's naturally that talent has to be much more improvement and enhanced all the wat fom fresh graduates from colleges to more experienced folks in the in the actual profession. >> What about when you say enhancing the pool talent you have. Could it also include productivity improvements, qualitative productivity improvements in the tools that makes machine learning more accessible at any level? The old story of rising obstruction layers where deep learning might help design statistical models by doing future engineering and optimizing the search for the best model, that sort of stuff. >> Tools are very, very hopeful. There are so many. We have from our tools to python tools to psychic and all of that which can help the data scientist. The key part is the knowledge of the data scientist so data science, you need the algorithm, the statistical background, then you need your applications software development background and then you also need the domestics for engineering background. You have to bring all of them together. >> We don't have too many Michaelangelos who are these all around geniuses. There's the issue of, how do you to get them to work more effectively together and then assuming even each of those are in short supply, how do you make them more productive? >> So making them more productive is by giving them the right tools and resources to work with. I think that's the best way to do it, and in some cases in my organization, we just say, okay we know that a particular person is skilled is up skilled in certain technologies and certain skill sets and then give them all the tools and resources for them to go on build. There's a constant education training process that goes through that we in fact, we have our entire Watson ED platform that can be learned on Kosera today. >> George: Interesting. >> So people can go and learn how to build a platform from a Kosera. >> When we start talking with clients and with vendors, things we hear is that and we were kind of I think early that calling foul but in the open source infrastructure big data infrastructure this notion of mix-and-match and roll your own pipeline sounded so alluring, but in the end it was only the big Internet companies and maybe some big banks and telcos that had the people to operate that stuff and probably even fewer who could build stuff on it. Do we do we need to up level or simplify some of those roles because mainstream companies can't have enough or won't will have enough data scientists or other roles needed to make that whole team work >> I think it will be a combination of both one is we need to up school our existing students with the stem background, that's one thing and the other aspect is, how do you up scale your existing folks in your companies with the latest tools and how can you automate more things so that people who may not be schooled will still be able to use the tool to deliver other things but they don't have to go to a rigorous curriculum to actually be able to deal with it. >> So what does that look like? Give us an example. >> Think of tools like today. There are a lot of BI folks who can actually build. BI is usually your trends and graphs and charts that comes out of the data which are simple things. So they understand the distribution and so on and so forth but they may not know what is the random model. If you look at tools today, that actually gives you to build them, once you give the data to that model, it actually gives you the outputs so they don't really have to go dig deep I have to understand the decision tree model and so on and so forth. They have the data, they can give the data, tools like that. There are so many different tools which would actually give you the outputs and then they can actually start building app, the analytics application on top of that rather than being worried about how do I write 1000 line code or 2000 line code to actually build that model itself. >> The inbuilt machine learning models in and intend, integrated to like pentaho or what's another example. I'm trying to think, I lost my, I having a senior moment. These happen too often now. >> We do have it in our own data science tools. We already have those models supported. You can actually go and call those in your web portal and be able to call the data and then call the model and then you'll get all that. >> George: Splank has something like that. >> Splank does, yes. >> I don't know how functional it is but it seems to be oriented towards like someone who built a dashboard can sort of wire up a model, it gives you an example of what type of predictions or what type of data you need. >> True, in the Splank case, I think it is more of BI tool actually supporting a level of data science moral support on the back. I do not know, maybe I have to look at this but in our case we have a complete data science experience where you actually start from the minute the data gets ingested, you can actually start the storage, the transformation, the analytics and all of that can be done in less than 10 lines of coding. You can just actually do the whole thing. You just call those functions then it will the right there in front of you. So in twin you can do that. That I think is much more powerful and there are tools, there are many many tools today. >> So you're saying that data science experience is an enter in pipeline and therefore can integrate what were boundaries between separate products. >> The boundary is becoming narrower and narrower in some sense. You can go all the way from data ingestion to the analytics in just few clicks or few lines of course. That's what's happening today. Integrated experience if you will. >> That's different from the specialized skills where you might have a tri-factor, prexada or something similar as for the wrangling and then something else for sort of the the visualizations like Altracks or Tavlo and then into modeling. >> A year or so ago, most of data scientists try to spend a lot of time doing data wrangling because some of the models, they can actually call very directly but the wrangling is actually where they spend their time. How do you get the data crawl the data, cleanse the data, etc. That is all now part of our data platform. It is already integrated into the platform so you don't have to go through some of these things. >> Where are you finding the first success for that tool suite? >> Today it is almost integrated with, for instance, I had a case where we exchange the data we integrate that into what's in the Watson data platform and the Watson APIs is a layer above us in the platform where we actually use the analytics tools, more advanced AI tools but the simple machinery models and so on and so forth is already integrated into as part of the Watson data platform. It is going to become an integrated experience through and through. >> To connect data science experience into eWatson IoT platform and maybe a little higher at this quasi-solution layer. >> Correct, exactly. >> Okay, interesting. >> We are doing that today and given the fact that we have so much happening on the edge side of things which means mission critical systems today are expecting stream analysts to get to get insights right there and then be able to provide the outcomes at the edge rather than pushing all the data up to your cloud and then bringing it back down. >> Let's talk about edge versus cloud. Obviously, we can't for latency and band width reasons we can't forward all the data to the cloud, but there's different use cases. We were talking to Matasa Harry at Sparks Summit and one of the use cases he talked about was video. You can't send obviously all the video back and you typically on an edge device wouldn't have heavy-duty machine learning, but for video camera, you might want to learn what is anomalous or behavior call out for that camera. Help us understand some of the different use cases and how much data do you bring back and how frequently do retrain the models? >> In the case of video, it's so true that you want to do a lot of any object ignition and so on and so forth in the video itself. We have tools today, we have cameras outside where if a van goes it detect the particular object in the video live. Realtime streaming analytics so we can do that today. What I'm seeing today in the market is, in the transaction between the edge and the cloud. We believe edge is an extension of the cloud, closer to the asset or device and we believe that models are going to get pushed from the cloud, closer to the edge because the compute capacity and storage and the networking capacity are all improving. We are pushing more and more computing to their devices. >> When you talk about pushing more of the processing. you're talking more about predicts and inferencing then the training. >> Correct. >> Okay. >> I don't think I see so much of the training needs to be done at the edge. >> George: You don't see it. >> No, not yet at least. We see the training happening in the cloud and then once a train, the model has been trained, then you come to a steady, steady model and then that is the model you want to push. When you say model, it could be a bunch of coefficients. That could be pushed onto the edge and then when a new data comes in, you evaluate, make decisions on that, create insights and push it back as actions to the asset and then that data can be pushed back into the cloud once a day or once in a week, whatever that is. Whatever the capacity of the device you have and we believe that edge can go across multiple scales. We believe it could be as small with 128 MB it could be one or two which I see sitting in your local data center on the premise. >> I've had to hear examples of 32 megs in elevators. >> Exactly. >> There might be more like a sort of bandwidth and latency oriented platform at the edge and then throughput and an volume in the cloud for training. And then there's the issue of do you have a model at the edge that corresponds to that instance of a physical asset and then do you have an ensemble meaning, the model that maps to that instance, plus a master canonical model. Does that work for? >> In some cases, I think it'll be I think they have master canonical model and other subsidiary models based on what the asset, it could be a fleet so you in the fleet of assets which you have, you can have, does one asset in the fleet behave similar to another asset in the fleet then you could build similarity models in that. But then there will also be a model to look at now that I have to manage this fleet of assets which will be a different model compared to action similarity model, in terms of operations, in terms of optimization if I want to make certain operations of that asset work more efficiently, that model could be completely different with when compared to when you look at similarity of one model or one asset with another. >> That's interesting and then that model might fit into the information technology systems, the enterprise systems. Let's talk, I want to go get a little lower level now about the issue of intellectual property, joint development and sharing and ownership. IBM it's a nuanced subject. So we get different sort of answers, definitive answers from different execs, but at this high level, IBM says unlike Google and Facebook we will not take your customer data and make use of it but there's more to it than that. It's not as black-and-white. Help explain that for so us. >> The way you want to think is I would definitely paired back what our chairman always says customers' data is customers' data, customer insights is customer insights so they way we look at it is if you look at a black box engine, that could be your analytics engine, whatever it is. The data is your inputs and the insights are our outputs so the insights and outputs belong to them. we don't take their data and marry it with somebody else's data and so forth but we use the data to train the models and the model which is an abstract version of what that engine should be and then more we train the more better the model becomes. And then we can then use across many different customers and as we improve the models, we might go back to the same customers and hey we have an improved model you want to deploy this version rather than the previous version of the model we have. We can go to customer Y and say, here is a model which we believe it can take more of your data and fine tune that model again and then give it back to them. It is true that we don't actually take their data and share the data or the insights from one customer X to another customer Y but the models that make it better. How do you make that model more intelligent is what out job is and that's what we do. >> If we go with precise terminology, it sounds like when we talk about the black box having learned from the customer data and the insights also belonging to the customer. Let's say one of the examples we've heard was architecture engineering consulting for large capital projects has a model that's coming obviously across that vertical but also large capital projects like oil and gas exploration, something like that. There, the model sounds like it's going to get richer with each engagement. And let's pin down so what in the model is sort of not exposed to the next customer and what part of the model that has gotten richer does the next customer get the balance of? >> When we actually build a model, when we pass the data, in some cases, customer X data, the model is built out of customer X data may not sometimes work with the customer Y's data so in which case you actually build it from scratch again. Sometimes it doesn't. In some case it does help because of the similarity of the data in some instance because if the data from company X in oil gas is similar to company Y in oil gas, sometimes the data could be similar so in which case when you train that model, it becomes more efficient and the efficiency goes back to both customers. we will do that but there are places where it would really not work. What we are trying to do is. We are in fact trying to build some kind of knowledge bundles where we can actually what used to be a long process to train the model can ow shortened using that knowledge bundle of what we have actually gained. >> George: Tell me more about how it works. >> In retail for instance, when we actually provide analytics, from any kind of IoT sense, whatever sense of data this comes in we train the model, we get analytics used for ads, pushing coupons, whatever it is. That knowledge, what you have gained off that retail, it could be models of models, it could be metamodels, whatever you built. That can actually serve many different customers but the first customer who is trying to engage with us, you don't have any data to the model. It's almost starting from ground zero and so that would actually take a longer time when you are starting with a new industry and you don't have the data, it'll take you a longer time to understand what is that saturation point or optimization point where you think the model cannot go any further. In some cases, once you do that, you can take that saturated model or near saturated model and improve it based on more data that actually comes from different other segments. >> When you have a model that has gotten better with engagements and we've talked about the black box which produces the insights after taking in the customer data. Inside that black box there's like at the highest level we might call it the digital twin with the broad definition that we started with, then there's a data model which a data model which I guess could also be incorporated into the knowledge graft for the structure and then would it be fair to call the operational model the behavior? >> Yes, how does the system perform or behave with respect the data and the asset itself. >> And then underpinning that, the different models that correspond to the behaviors of different parts of this overall asset. So if we were to be really precise about this black box, what can move from one customer to the next and what what won't? >> The overall model, supposing I'm using a random data retrieval model, that remains but actual the coefficients are the feature rector, or whatever I use, that could be totally different for customers, depending on what kind of data they actually provide us. In data science or in analytics you have a whole platora of all the way from simple classification algorithms to very advanced predictive modeling algorithms. If you take the whole class when you start with a customer, you don't know which model is really going to work for a specific user case because the customer might come and can say, you might get some idea but you will not know exactly this is the model that will work. How you test it with one customer, that model could remain the same kind of use case for some of other customer, but that actual the coefficients the degree of the digital in some cases it might be two level decision trees, in others case it might be a six level decision tree. >> It is not like you take the model and the features and then just let different customers tweak the coefficients for the features. >> If you can do that, that will be great but I don't know whether you can really do it the data is going to change. The data is definitely going to change at some point of time but in certain cases it might be directly correlated where it can help, in certain cases it might not help. >> What I'm taking away is this is fundamentally different from traditional enterprise applications where you could standardize business processes and the transactional data that they were producing. Here it's going to be much more bespoke because I guess the processes, the analytic processes are not standardized. >> Correct, every business processes is unique for a business. >> The accentures of the world we're trying to tell people that when SAP shipped packaged processes, which were pretty much good enough, but that convince them to spend 10 times as much as the license fee on customization. But is there a qualitative difference between the processes here and the processes in the old ERP era? I think it's kind of different in the ERP era and the processes, we are more talking about just data management. Here we're talking about data science which means in the data management world, you're just moving data or transforming data and things like that, that's what you're doing. You're taking the data. transforming to some other form and then you're doing basic SQL queries to get some response, blah blah blah. That is a standard process that is not much of intelligence attached to it but now you are trying to see from the data what kind of intelligence can you derive by modeling the characteristics of the data. That becomes a much tougher problem so it now becomes one level higher of intelligence that you need to capture from the data itself that you want to serve a particular outcome from the insights you get from is model. >> This sounds like the differences are based on one different business objectives and perhaps data that's not as uniform that you would in enterprise applications, you would standardize the data here, if it's not standardized. >> I think because of the varied the disparity of the businesses and the kinds of verticals and things like that you're looking at, to get complete unified business model, is going to be extremely difficult. >> Last question, back-office systems the highest level they got to were maybe the CFO 'cause you had a sign off on a lot of the budget for the license and a much much bigger budget for the SI but he was getting something that was like close you quarter in three days or something instead of two weeks. It was a control function. Who do you sell to now for these different systems and what's the message, how much more strategic how do you sell the business impact differently? >> The platforms we directly interact with the CIO and CTOs or the head of engineering. And the actual solutions or the insights, we usually sell it to the COOs or the operational folks. So because the COO is responsible for showing you productivity, efficiency, how much of savings can you do on the bottom line top line. So the insights would actually go through the COOs or in some sense go through their CTOs to COOs but the actual platform itself will go to the enterprise IT folks in that order. >> This sounds like it's a platform and a solution sell which requires, is that different from the sales motions of other IBM technologies or is this a new approach? >> IBM is transforming on its way. The days where we believe that all the strategies and predictives that we are aligned towards, that actually needs to be the key goal because that's where the world is going. There are folks who, like Jeff Boaz talks about in the olden days you need 70 people to sell or 70% of the people to sell a 30% product. Today it's a 70% product and you need 30% to actually sell the product. The model is completely changing the way we interact with customers. So I think that's what's going to drive. We are transforming that in that area. We are becoming more conscious about all the strategy operations that we want to deliver to the market we want to be able to enable our customers with a much broader value proposition. >> With the industry solutions group and the Global Business Services teams work on these solutions. They've already been selling, line of business CXO type solutions. So is this more of the same, it's just better or is this really higher level than IBM's ever gotten in terms of strategic value? >> This is possibly in decades I would say a high level of value which come from a strategic perspective. >> Okay, on that note Veeru, we'll call it a day. This is great discussion and we look forward to writing it up and clipping all the videos and showering the internet with highlights. >> Thank you George. Appreciate it. >> Hopefully I will get you back soon. >> I was a pleasure, absolutely. >> With that, this George Gilbert. We're in our Palo Alto studio for wiki bond and theCUBE and we've been talking to Veeru Ramaswamy who's VP of Watson IoT platform and we look forward to coming back with Veeru sometime soon. (upbeat music)